R Markdown

library(readr)

rpx <- read_csv("~/Box/CogNeuroLab/Aging Decision Making R01/Data/Actigraphy/Combined Export File.csv")
## Warning: Missing column names filled in: 'X30' [30]
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   analysis_name = col_character(),
##   subject_id = col_integer(),
##   data_start_date = col_character(),
##   data_start_time = col_time(format = ""),
##   interval_type = col_character(),
##   interval_number = col_character(),
##   start_date = col_character(),
##   start_time = col_character(),
##   end_date = col_character(),
##   end_time = col_character(),
##   X30 = col_character()
## )
## See spec(...) for full column specifications.
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)
## Warning: 12 parsing failures.
## row # A tibble: 5 x 5 col     row col      expected  actual file                                     expected   <int> <chr>    <chr>     <chr>  <chr>                                    actual 1  9621 subject… an integ… Pilot  '~/Box/CogNeuroLab/Aging Decision Makin… file 2  9622 subject… an integ… Pilot  '~/Box/CogNeuroLab/Aging Decision Makin… row 3  9623 subject… an integ… Pilot  '~/Box/CogNeuroLab/Aging Decision Makin… col 4  9624 subject… an integ… Pilot  '~/Box/CogNeuroLab/Aging Decision Makin… expected 5  9625 subject… an integ… Pilot  '~/Box/CogNeuroLab/Aging Decision Makin…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
efficiency <- aggregate(efficiency ~ subject_id, rpx, mean, na.action = na.omit)
sleep_time <- aggregate(sleep_time ~ subject_id, rpx, mean, na.action = na.omit)
percent_wake <- aggregate(percent_wake ~ subject_id, rpx, mean, na.action = na.omit)
onset_latency <- aggregate(onset_latency ~ subject_id, rpx, mean, na.action = na.omit)
total_ac <- aggregate(total_ac ~ subject_id, rpx, mean, na.action = na.omit)

rpx2 <- merge(efficiency, sleep_time, by = 'subject_id')
rpx2 <- merge(rpx2, percent_wake, by = 'subject_id')
rpx2 <- merge(rpx2, onset_latency, by = 'subject_id')
rpx2 <- merge(rpx2, total_ac, by = 'subject_id')
head(rpx2)
##   subject_id efficiency sleep_time percent_wake onset_latency total_ac
## 1      30003   60.87938   256.9579     44.57318     28.659375 224642.1
## 2      30004   76.92000   335.6575     36.54286      9.823333 133108.1
## 3      30008   72.66813   341.6363     36.91493     40.873750 121651.0
## 4      30009   75.77800   308.7515     39.15000      8.966667 186779.0
## 5      30012   76.95733   311.9442     39.12548     10.124667 177709.2
## 6      30015   75.60062   315.6144     39.06553     25.420313 135299.1
cr <- read_csv('~/Box/CogNeuroLab/Aging Decision Making R01/Data/CR/circadian_rhythms_2019-09-07.csv')
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   record_id = col_integer(),
##   L5_starttime = col_time(format = ""),
##   M10_starttime = col_time(format = "")
## )
## See spec(...) for full column specifications.
cr$actquot <- cr$actamp/cr$actmesor

neuro <- read_csv('~/Box/CogNeuroLab/Aging Decision Making R01/Data/Neuropsych/AgingDecMemNeuropsyc_DATA_2019-06-12_0708.csv')
## Parsed with column specification:
## cols(
##   .default = col_integer(),
##   education = col_double(),
##   cvlt_b_zscore = col_double(),
##   cvlt_sdelay_recall_zscore = col_double(),
##   cvlt_sdelay_cued_zscore = col_double(),
##   cvlt_ldelay_recall_zscore = col_double(),
##   cvlt_ldelay_cue_recall_zscore = col_double(),
##   repetitions2_828 = col_double(),
##   intrusions2_c77 = col_double(),
##   cvlt_recognition_hits_zscore = col_double(),
##   cvlt_recognition_fp_zscore = col_double(),
##   cvlt_zscore = col_double(),
##   vc_per_rank = col_double(),
##   vc_zscore = col_double(),
##   ds_per_rank = col_double(),
##   ds_zscore = col_double(),
##   i_stroop_agecorrected = col_double(),
##   stoop_i_zscore = col_double(),
##   cowat_mean = col_double(),
##   cowat_sd = col_double(),
##   cowat_zscore = col_double()
##   # ... with 4 more columns
## )
## See spec(...) for full column specifications.
bct <- read_csv('~/Box/CogNeuroLab/Aging Decision Making R01/Analysis/bct/bct_x.csv')
## Parsed with column specification:
## cols(
##   record_id = col_integer(),
##   wb_clustering_x = col_double(),
##   wb_efficiency_x = col_double(),
##   wb_modularity_x = col_double(),
##   wb_participation_x = col_double(),
##   wb_betweenness_x = col_double(),
##   dmn_clustering_x = col_double(),
##   dmn_efficiency_x = col_double(),
##   dmn_modularity_x = col_double(),
##   dmn_participation_x = col_double(),
##   dmn_betweenness_x = col_double(),
##   fpn_clustering_x = col_double(),
##   fpn_efficiency_x = col_double(),
##   fpn_modularity_x = col_double(),
##   fpn_participation_x = col_double(),
##   fpn_betweenness_x = col_double()
## )
d <- merge(cr, neuro, by='record_id', all=TRUE)
d <- merge(d, bct, by='record_id', all=TRUE)
d <- merge(d, rpx2, by.x = 'record_id', by.y = 'subject_id', all=TRUE)
d$group <- factor(ifelse(d$record_id < 40000, 0, 1))

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You can also embed plots, for example:

library(formattable)

d %>%
  select(age, IS:fact, group) %>%
  group_by(group) %>%
  summary() 
##       age              IS               IV               RA        
##  Min.   :18.00   Min.   :0.0300   Min.   :0.3800   Min.   :0.1700  
##  1st Qu.:21.00   1st Qu.:0.3425   1st Qu.:0.6700   1st Qu.:0.8100  
##  Median :60.00   Median :0.4400   Median :0.8700   Median :0.8700  
##  Mean   :46.81   Mean   :0.4305   Mean   :0.8819   Mean   :0.8340  
##  3rd Qu.:68.00   3rd Qu.:0.5275   3rd Qu.:1.0475   3rd Qu.:0.9275  
##  Max.   :91.00   Max.   :0.7900   Max.   :1.6600   Max.   :1.0000  
##  NA's   :5       NA's   :16       NA's   :16       NA's   :16      
##        L5         L5_starttime           M10         M10_starttime    
##  Min.   : 0.000   Length:134        Min.   : 34.58   Length:134       
##  1st Qu.: 6.067   Class1:hms        1st Qu.:124.58   Class1:hms       
##  Median :10.370   Class2:difftime   Median :158.68   Class2:difftime  
##  Mean   :14.174   Mode  :numeric    Mean   :159.16   Mode  :numeric   
##  3rd Qu.:15.530                     3rd Qu.:186.56                    
##  Max.   :66.690                     Max.   :291.28                    
##  NA's   :16                         NA's   :16                        
##      actamp         actbeta             actphi          actmin      
##  Min.   :1.000   Min.   :  0.9178   Min.   :10.54   Min.   :0.0000  
##  1st Qu.:1.370   1st Qu.:  4.7452   1st Qu.:14.44   1st Qu.:0.0000  
##  Median :1.529   Median :  7.3160   Median :15.55   Median :0.1084  
##  Mean   :1.515   Mean   : 10.1289   Mean   :15.57   Mean   :0.1557  
##  3rd Qu.:1.703   3rd Qu.: 10.3520   3rd Qu.:16.60   3rd Qu.:0.2272  
##  Max.   :2.104   Max.   :132.4992   Max.   :19.57   Max.   :0.7636  
##  NA's   :17      NA's   :17         NA's   :17      NA's   :17      
##     actmesor        actupmesor      actdownmesor      actalph       
##  Min.   :0.5000   Min.   : 2.721   Min.   :17.09   Min.   :-1.0000  
##  1st Qu.:0.7997   1st Qu.: 6.393   1st Qu.:22.30   1st Qu.:-0.5764  
##  Median :0.9092   Median : 7.704   Median :23.29   Median :-0.4825  
##  Mean   :0.9132   Mean   : 7.737   Mean   :23.40   Mean   :-0.4631  
##  3rd Qu.:0.9917   3rd Qu.: 8.805   3rd Qu.:24.62   3rd Qu.:-0.3996  
##  Max.   :1.3158   Max.   :14.749   Max.   :28.51   Max.   : 0.9533  
##  NA's   :17       NA's   :17       NA's   :17      NA's   :17       
##  actwidthratio         rsqact             fact        group 
##  Min.   :0.09767   Min.   :0.03402   Min.   : 178.4   0:60  
##  1st Qu.:0.62645   1st Qu.:0.27017   1st Qu.:2664.9   1:74  
##  Median :0.65938   Median :0.35294   Median :3913.0         
##  Mean   :0.65251   Mean   :0.33846   Mean   :4016.6         
##  3rd Qu.:0.69286   3rd Qu.:0.41095   3rd Qu.:5079.7         
##  Max.   :0.79392   Max.   :0.57014   Max.   :9548.0         
##  NA's   :17        NA's   :17        NA's   :17
d %>%
  filter(group == 1) %>%
  select(age, IS:fact) %>%
  summary()
##       age              IS               IV               RA        
##  Min.   :60.00   Min.   :0.1000   Min.   :0.3800   Min.   :0.2700  
##  1st Qu.:64.00   1st Qu.:0.3775   1st Qu.:0.6375   1st Qu.:0.8175  
##  Median :67.00   Median :0.5050   Median :0.8350   Median :0.8600  
##  Mean   :68.78   Mean   :0.4667   Mean   :0.8608   Mean   :0.8331  
##  3rd Qu.:72.00   3rd Qu.:0.5525   3rd Qu.:1.0350   3rd Qu.:0.9150  
##  Max.   :91.00   Max.   :0.7900   Max.   :1.6600   Max.   :0.9700  
##  NA's   :5       NA's   :10       NA's   :10       NA's   :10      
##        L5        L5_starttime           M10         M10_starttime    
##  Min.   : 2.18   Length:74         Min.   : 34.58   Length:74        
##  1st Qu.: 5.80   Class1:hms        1st Qu.:113.32   Class1:hms       
##  Median :10.11   Class2:difftime   Median :149.50   Class2:difftime  
##  Mean   :13.09   Mode  :numeric    Mean   :149.84   Mode  :numeric   
##  3rd Qu.:15.24                     3rd Qu.:177.38                    
##  Max.   :65.86                     Max.   :291.28                    
##  NA's   :10                        NA's   :10                        
##      actamp         actbeta             actphi          actmin      
##  Min.   :1.000   Min.   :  0.9178   Min.   :10.54   Min.   :0.0000  
##  1st Qu.:1.278   1st Qu.:  4.6566   1st Qu.:13.87   1st Qu.:0.0331  
##  Median :1.493   Median :  8.4566   Median :14.73   Median :0.1141  
##  Mean   :1.472   Mean   : 12.4544   Mean   :14.80   Mean   :0.1659  
##  3rd Qu.:1.682   3rd Qu.: 13.0742   3rd Qu.:15.69   3rd Qu.:0.2203  
##  Max.   :2.027   Max.   :132.4992   Max.   :18.64   Max.   :0.7425  
##  NA's   :11      NA's   :11         NA's   :11      NA's   :11      
##     actmesor        actupmesor      actdownmesor      actalph       
##  Min.   :0.5000   Min.   : 2.721   Min.   :17.09   Min.   :-0.7976  
##  1st Qu.:0.7894   1st Qu.: 6.104   1st Qu.:21.60   1st Qu.:-0.5458  
##  Median :0.8839   Median : 7.081   Median :22.49   Median :-0.4567  
##  Mean   :0.9018   Mean   : 7.108   Mean   :22.50   Mean   :-0.4255  
##  3rd Qu.:1.0036   3rd Qu.: 7.956   3rd Qu.:23.55   3rd Qu.:-0.3507  
##  Max.   :1.3158   Max.   :14.749   Max.   :27.02   Max.   : 0.9533  
##  NA's   :11       NA's   :11       NA's   :11      NA's   :11       
##  actwidthratio         rsqact             fact       
##  Min.   :0.09767   Min.   :0.03402   Min.   : 178.4  
##  1st Qu.:0.61407   1st Qu.:0.27409   1st Qu.:2719.7  
##  Median :0.65097   Median :0.36583   Median :4119.7  
##  Mean   :0.64128   Mean   :0.34312   Mean   :4131.0  
##  3rd Qu.:0.68379   3rd Qu.:0.41425   3rd Qu.:5168.6  
##  Max.   :0.79392   Max.   :0.57014   Max.   :9548.0  
##  NA's   :11        NA's   :11        NA's   :11
d.mlt <- melt(select(d, record_id, group, IS:actquot, -rsqact, -fnlrgact, -L5_starttime, -M10_starttime), id.vars=c('record_id', 'group'))

ggplot(data = d.mlt, aes(x = variable, y = value, group = group)) + 
  geom_violin(aes(fill = group)) + 
  facet_wrap( ~ variable,  scales='free') + 
  theme_minimal() +
  scale_fill_manual(values = brewer.pal(8, "Paired")[7:8], name = 'Age Group', labels = c('Young Adults', 'Older Adults')) + 
  xlab(element_blank()) + ylab(element_blank())
## Warning: Removed 267 rows containing non-finite values (stat_ydensity).

#Effect of age on circadian variables
#Older Adults
summary(lm(actamp ~ age, data = d[d$group == 1,])) #p = 0.0119
## 
## Call:
## lm(formula = actamp ~ age, data = d[d$group == 1, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.52866 -0.15392  0.00052  0.18067  0.49516 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.392135   0.357238   6.696 1.02e-08 ***
## age         -0.013492   0.005193  -2.598   0.0119 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2641 on 57 degrees of freedom
##   (15 observations deleted due to missingness)
## Multiple R-squared:  0.1059, Adjusted R-squared:  0.0902 
## F-statistic:  6.75 on 1 and 57 DF,  p-value: 0.01191
summary(lm(fact ~ age, data = d[d$group == 1,])) #p = 0.024507
## 
## Call:
## lm(formula = fact ~ age, data = d[d$group == 1, ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4368.1 -1241.7  -205.1  1115.5  5092.7 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10375.77    2712.08   3.826 0.000326 ***
## age           -91.08      39.42  -2.310 0.024507 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2005 on 57 degrees of freedom
##   (15 observations deleted due to missingness)
## Multiple R-squared:  0.08563,    Adjusted R-squared:  0.06959 
## F-statistic: 5.338 on 1 and 57 DF,  p-value: 0.02451
summary(lm(actupmesor ~ age, data = d[d$group == 1,])) #NS
## 
## Call:
## lm(formula = actupmesor ~ age, data = d[d$group == 1, ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5526 -0.9929 -0.1135  0.9229  7.4358 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.84936    2.53471   3.886 0.000268 ***
## age         -0.03963    0.03684  -1.076 0.286601    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.874 on 57 degrees of freedom
##   (15 observations deleted due to missingness)
## Multiple R-squared:  0.0199, Adjusted R-squared:  0.002701 
## F-statistic: 1.157 on 1 and 57 DF,  p-value: 0.2866
summary(lm(actdownmesor ~ age, data = d[d$group == 1,])) #NS
## 
## Call:
## lm(formula = actdownmesor ~ age, data = d[d$group == 1, ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3648 -0.9937  0.0731  1.0261  4.4114 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 21.834009   2.477457   8.813 3.12e-12 ***
## age          0.009744   0.036013   0.271    0.788    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 57 degrees of freedom
##   (15 observations deleted due to missingness)
## Multiple R-squared:  0.001283,   Adjusted R-squared:  -0.01624 
## F-statistic: 0.07321 on 1 and 57 DF,  p-value: 0.7877
summary(lm(RA ~ age, data = d[d$group == 1,])) #NS
## 
## Call:
## lm(formula = RA ~ age, data = d[d$group == 1, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.47173 -0.01832  0.02736  0.08782  0.12931 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.8248210  0.1692245   4.874 8.84e-06 ***
## age         0.0002601  0.0024547   0.106    0.916    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1266 on 58 degrees of freedom
##   (14 observations deleted due to missingness)
## Multiple R-squared:  0.0001935,  Adjusted R-squared:  -0.01704 
## F-statistic: 0.01123 on 1 and 58 DF,  p-value: 0.916
summary(lm(IS ~ age, data = d[d$group == 1,])) #NS
## 
## Call:
## lm(formula = IS ~ age, data = d[d$group == 1, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.37044 -0.11336  0.02350  0.09238  0.30658 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.674048   0.201030   3.353  0.00141 **
## age         -0.002979   0.002916  -1.021  0.31128   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1503 on 58 degrees of freedom
##   (14 observations deleted due to missingness)
## Multiple R-squared:  0.01767,    Adjusted R-squared:  0.0007343 
## F-statistic: 1.043 on 1 and 58 DF,  p-value: 0.3113
summary(lm(IV ~ age, data = d[d$group == 1,])) # p = 0.08
## 
## Call:
## lm(formula = IV ~ age, data = d[d$group == 1, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45774 -0.20770 -0.03235  0.18472  0.72099 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 0.230124   0.363957   0.632   0.5297  
## age         0.009206   0.005279   1.744   0.0865 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2722 on 58 degrees of freedom
##   (14 observations deleted due to missingness)
## Multiple R-squared:  0.04982,    Adjusted R-squared:  0.03343 
## F-statistic: 3.041 on 1 and 58 DF,  p-value: 0.08649
#Younger Adults
summary(lm(actamp ~ age, data = d[d$group == 0,])) #NS
## 
## Call:
## lm(formula = actamp ~ age, data = d[d$group == 0, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.54283 -0.10441 -0.01082  0.14652  0.54349 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.359623   0.194918   6.975 5.42e-09 ***
## age         0.009584   0.008938   1.072    0.289    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2401 on 52 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.02163,    Adjusted R-squared:  0.002818 
## F-statistic:  1.15 on 1 and 52 DF,  p-value: 0.2885
summary(lm(fact ~ age, data = d[d$group == 0,])) #NS
## 
## Call:
## lm(formula = fact ~ age, data = d[d$group == 0, ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3010.1  -964.0  -330.9   774.8  4176.4 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  1992.72    1393.37   1.430    0.159
## age            87.93      63.89   1.376    0.175
## 
## Residual standard error: 1716 on 52 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.03515,    Adjusted R-squared:  0.01659 
## F-statistic: 1.894 on 1 and 52 DF,  p-value: 0.1746
summary(lm(actupmesor ~ age, data = d[d$group == 0,])) #p = 0.0272
## 
## Call:
## lm(formula = actupmesor ~ age, data = d[d$group == 0, ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3138 -1.0715 -0.2811  1.2755  3.9007 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.54640    1.37266   8.412 2.86e-11 ***
## age         -0.14306    0.06294  -2.273   0.0272 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 52 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.09037,    Adjusted R-squared:  0.07287 
## F-statistic: 5.166 on 1 and 52 DF,  p-value: 0.0272
summary(lm(actdownmesor ~ age, data = d[d$group == 0,])) #p = 0.0206
## 
## Call:
## lm(formula = actdownmesor ~ age, data = d[d$group == 0, ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3415 -1.1730 -0.1704  0.8760  3.6074 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 27.24931    1.19120  22.876   <2e-16 ***
## age         -0.13042    0.05462  -2.388   0.0206 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.467 on 52 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.09881,    Adjusted R-squared:  0.08148 
## F-statistic: 5.702 on 1 and 52 DF,  p-value: 0.02062
summary(lm(RA ~ age, data = d[d$group == 0,])) #NS
## 
## Call:
## lm(formula = RA ~ age, data = d[d$group == 0, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.66963 -0.03269  0.04617  0.09231  0.17271 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.768697   0.118702   6.476 3.39e-08 ***
## age         0.003084   0.005443   0.567    0.573    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1462 on 52 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.006135,   Adjusted R-squared:  -0.01298 
## F-statistic: 0.321 on 1 and 52 DF,  p-value: 0.5734
summary(lm(IS ~ age, data = d[d$group == 0,])) #NS
## 
## Call:
## lm(formula = IS ~ age, data = d[d$group == 0, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.35814 -0.05518  0.01649  0.06277  0.19369 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.3796958  0.0884178   4.294 7.67e-05 ***
## age         0.0003673  0.0040543   0.091    0.928    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1089 on 52 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.0001578,  Adjusted R-squared:  -0.01907 
## F-statistic: 0.008207 on 1 and 52 DF,  p-value: 0.9282
summary(lm(IV ~ age, data = d[d$group == 0,])) #NS
## 
## Call:
## lm(formula = IV ~ age, data = d[d$group == 0, ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51102 -0.16389  0.00232  0.13482  0.63379 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.719899   0.204832   3.515 0.000922 ***
## age         0.008704   0.009392   0.927 0.358350    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2523 on 52 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.01625,    Adjusted R-squared:  -0.002671 
## F-statistic: 0.8588 on 1 and 52 DF,  p-value: 0.3584
# Stability from day to day
t.test(IS ~ group, data = d) # 0.00142
## 
##  Welch Two Sample t-test
## 
## data:  IS by group
## t = -3.2717, df = 112.45, p-value = 0.00142
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.12704321 -0.03120911
## sample estimates:
## mean in group 0 mean in group 1 
##       0.3875926       0.4667188
t.test(IV ~ group, data = d)
## 
##  Welch Two Sample t-test
## 
## data:  IV by group
## t = 0.95856, df = 114.94, p-value = 0.3398
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.0493299  0.1418415
## sample estimates:
## mean in group 0 mean in group 1 
##       0.9070370       0.8607813
t.test(RA ~ group, data = d)
## 
##  Welch Two Sample t-test
## 
## data:  RA by group
## t = 0.070789, df = 111.5, p-value = 0.9437
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05060833  0.05435833
## sample estimates:
## mean in group 0 mean in group 1 
##        0.835000        0.833125
t.test(actamp ~ group, data = d)
## 
##  Welch Two Sample t-test
## 
## data:  actamp by group
## t = 1.951, df = 115, p-value = 0.05349
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.001432771  0.189215179
## sample estimates:
## mean in group 0 mean in group 1 
##        1.565671        1.471780
t.test(fact ~ group, data = d)
## 
##  Welch Two Sample t-test
## 
## data:  fact by group
## t = -0.7062, df = 114.95, p-value = 0.4815
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -942.4018  447.0391
## sample estimates:
## mean in group 0 mean in group 1 
##        3883.271        4130.952
# Morningness preference
t.test(actphi ~ group, data = d) # p = 1.541e-08
## 
##  Welch Two Sample t-test
## 
## data:  actphi by group
## t = 6.0986, df = 112.66, p-value = 1.541e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.116990 2.191975
## sample estimates:
## mean in group 0 mean in group 1 
##        16.45795        14.80347
t.test(actupmesor ~ group, data = d) # p = 8.015e-05
## 
##  Welch Two Sample t-test
## 
## data:  actupmesor by group
## t = 4.0923, df = 113.66, p-value = 8.015e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.702991 2.022265
## sample estimates:
## mean in group 0 mean in group 1 
##        8.470703        7.108075
t.test(actdownmesor~ group, data = d) # p = 4.229e-09
## 
##  Welch Two Sample t-test
## 
## data:  actdownmesor by group
## t = 6.3603, df = 115, p-value = 4.229e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.340180 2.552494
## sample estimates:
## mean in group 0 mean in group 1 
##        24.44519        22.49886
wilcox.test(IS ~ group, data=d) #sig
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  IS by group
## W = 1101, p-value = 0.00071
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(IV ~ group, data=d)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  IV by group
## W = 1963, p-value = 0.2052
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(RA ~ group, data=d)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  RA by group
## W = 1766, p-value = 0.8393
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(actamp ~ group, data=d) 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  actamp by group
## W = 2033, p-value = 0.06985
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(fact ~ group, data=d) 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  fact by group
## W = 1571, p-value = 0.4789
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(actphi ~ group, data=d) #sig
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  actphi by group
## W = 2691, p-value = 6.302e-08
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(actupmesor ~ group, data=d) #sig
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  actupmesor by group
## W = 2441, p-value = 5.274e-05
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(actdownmesor ~ group, data=d) #sig
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  actdownmesor by group
## W = 2734, p-value = 1.651e-08
## alternative hypothesis: true location shift is not equal to 0
#Sleep Measures
t.test(sleep_time ~ group, data = d) #NS
## 
##  Welch Two Sample t-test
## 
## data:  sleep_time by group
## t = -1.1941, df = 119.77, p-value = 0.2348
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -61.55525  15.24012
## sample estimates:
## mean in group 0 mean in group 1 
##        341.1519        364.3094
t.test(percent_wake ~ group, data = d) #p = 0.03961
## 
##  Welch Two Sample t-test
## 
## data:  percent_wake by group
## t = 2.0799, df = 123.44, p-value = 0.03961
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.08110572 3.27644073
## sample estimates:
## mean in group 0 mean in group 1 
##        39.14684        37.46807
t.test(onset_latency ~ group, data = d) #p = 0.0066
## 
##  Welch Two Sample t-test
## 
## data:  onset_latency by group
## t = -2.768, df = 109.26, p-value = 0.006626
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -15.876576  -2.627602
## sample estimates:
## mean in group 0 mean in group 1 
##        22.91220        32.16429
t.test(efficiency ~ group, data = d) #NS
## 
##  Welch Two Sample t-test
## 
## data:  efficiency by group
## t = 0.9489, df = 123.23, p-value = 0.3445
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.250580  3.553696
## sample estimates:
## mean in group 0 mean in group 1 
##        72.21969        71.06813
summary(lm(trails_b_z_score ~ gender + IS, data = d)) # NS
## 
## Call:
## lm(formula = trails_b_z_score ~ gender + IS, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8257 -0.6877  0.2462  1.0938  2.8842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -1.4314     0.6079  -2.355   0.0203 *
## gender        0.4484     0.3109   1.442   0.1520  
## IS            1.2158     1.0799   1.126   0.2627  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.521 on 110 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.03988,    Adjusted R-squared:  0.02242 
## F-statistic: 2.284 on 2 and 110 DF,  p-value: 0.1067
summary(lm(ds_backward_score   ~ age + gender + IS, data = d)) # NS
## 
## Call:
## lm(formula = ds_backward_score ~ age + gender + IS, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0663 -2.0417 -0.4274  1.5670  6.5539 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.543308   1.082165  10.667   <2e-16 ***
## age         -0.005521   0.010548  -0.523    0.602    
## gender      -1.321268   0.534439  -2.472    0.015 *  
## IS          -0.169486   1.908391  -0.089    0.929    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.604 on 109 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.06345,    Adjusted R-squared:  0.03767 
## F-statistic: 2.461 on 3 and 109 DF,  p-value: 0.06646
summary(lm(cvlt_ldelay_recall_zscore  ~ IS, data = d)) # p = 0.08899
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ IS, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3598 -0.6626  0.1178  0.6552  2.1552 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   1.1524     0.4261   2.705  0.00896 **
## IS           -1.4956     0.8646  -1.730  0.08899 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9988 on 58 degrees of freedom
##   (74 observations deleted due to missingness)
## Multiple R-squared:  0.04906,    Adjusted R-squared:  0.03266 
## F-statistic: 2.992 on 1 and 58 DF,  p-value: 0.08899
summary(lm(trails_b_z_score ~ actupmesor^2 + actupmesor, data = d)) 
## 
## Call:
## lm(formula = trails_b_z_score ~ actupmesor^2 + actupmesor, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6518 -0.6030  0.2353  1.0893  2.8379 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -1.10264    0.60152  -1.833   0.0695 .
## actupmesor   0.11859    0.07535   1.574   0.1184  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.527 on 110 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.02203,    Adjusted R-squared:  0.01313 
## F-statistic: 2.477 on 1 and 110 DF,  p-value: 0.1184
summary(lm(ds_backward_score   ~ actupmesor^2 + actupmesor, data = d)) 
## 
## Call:
## lm(formula = ds_backward_score ~ actupmesor^2 + actupmesor, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3618 -1.6874 -0.3038  1.9018  6.1068 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   8.1689     1.0372   7.876 2.59e-12 ***
## actupmesor    0.1148     0.1293   0.888    0.377    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.641 on 110 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.007113,   Adjusted R-squared:  -0.001913 
## F-statistic: 0.788 on 1 and 110 DF,  p-value: 0.3766
summary(lm(cvlt_ldelay_recall_zscore  ~ actupmesor^2 + actupmesor, data = d)) 
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ actupmesor^2 + actupmesor, 
##     data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.46732 -0.52470  0.05424  0.59888  2.07220 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.65169    0.52735   1.236    0.222
## actupmesor  -0.03076    0.07152  -0.430    0.669
## 
## Residual standard error: 1.022 on 57 degrees of freedom
##   (75 observations deleted due to missingness)
## Multiple R-squared:  0.003235,   Adjusted R-squared:  -0.01425 
## F-statistic: 0.185 on 1 and 57 DF,  p-value: 0.6687
summary(lm(trails_b_z_score ~ actamp + group, data = d)) # p = 0.020397
## 
## Call:
## lm(formula = trails_b_z_score ~ actamp + group, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5327 -0.7351  0.1222  1.0960  2.5738 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -3.5357     0.8597  -4.113 7.61e-05 ***
## actamp        1.9839     0.5345   3.712 0.000326 ***
## group1        0.6526     0.2773   2.353 0.020397 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.444 on 109 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1335, Adjusted R-squared:  0.1176 
## F-statistic: 8.399 on 2 and 109 DF,  p-value: 0.000405
summary(lm(ds_backward_score ~ age + gender + group + actamp, data = d)) # p = 0.0631
## 
## Call:
## lm(formula = ds_backward_score ~ age + gender + group + actamp, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9785 -1.5818 -0.1768  1.5840  5.8367 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.10461    1.98697   5.085 1.57e-06 ***
## age         -0.07635    0.04496  -1.698   0.0924 .  
## gender      -1.31535    0.50828  -2.588   0.0110 *  
## group1       3.77811    2.14156   1.764   0.0806 .  
## actamp       1.76532    0.94007   1.878   0.0631 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.517 on 107 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1223, Adjusted R-squared:  0.08953 
## F-statistic: 3.729 on 4 and 107 DF,  p-value: 0.007009
summary(lm(cvlt_ldelay_recall_zscore  ~ actamp, data = d)) # NS
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.42885 -0.66983  0.08862  0.61260  2.32896 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   1.2095     0.7175   1.686   0.0973 .
## actamp       -0.5294     0.4803  -1.102   0.2750  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.013 on 57 degrees of freedom
##   (75 observations deleted due to missingness)
## Multiple R-squared:  0.02087,    Adjusted R-squared:  0.003688 
## F-statistic: 1.215 on 1 and 57 DF,  p-value: 0.275
summary(lm(trails_b_z_score ~ group + fact, data = d)) # p = 0.015
## 
## Call:
## lm(formula = trails_b_z_score ~ group + fact, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9129 -0.6277  0.1647  1.1082  2.2264 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -1.138e+00  3.525e-01  -3.229  0.00164 **
## group1       4.212e-01  2.830e-01   1.488  0.13953   
## fact         1.825e-04  7.421e-05   2.459  0.01549 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.492 on 109 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.07535,    Adjusted R-squared:  0.05838 
## F-statistic: 4.441 on 2 and 109 DF,  p-value: 0.01399
summary(lm(ds_backward_score   ~ age + gender + fact, data = d)) # NS
## 
## Call:
## lm(formula = ds_backward_score ~ age + gender + fact, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9338 -1.8536 -0.2575  1.7417  6.5404 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.0254573  1.0219391  10.789  < 2e-16 ***
## age         -0.0042325  0.0102207  -0.414  0.67961    
## gender      -1.3963157  0.5250697  -2.659  0.00902 ** 
## fact         0.0001338  0.0001301   1.028  0.30615    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.582 on 108 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.06796,    Adjusted R-squared:  0.04207 
## F-statistic: 2.625 on 3 and 108 DF,  p-value: 0.05418
summary(lm(cvlt_ldelay_recall_zscore  ~ actamp, data = d)) # NS
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.42885 -0.66983  0.08862  0.61260  2.32896 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   1.2095     0.7175   1.686   0.0973 .
## actamp       -0.5294     0.4803  -1.102   0.2750  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.013 on 57 degrees of freedom
##   (75 observations deleted due to missingness)
## Multiple R-squared:  0.02087,    Adjusted R-squared:  0.003688 
## F-statistic: 1.215 on 1 and 57 DF,  p-value: 0.275
summary(lm(trails_b_z_score ~ group + actupmesor, data = d)) # p = 0.019
## 
## Call:
## lm(formula = trails_b_z_score ~ group + actupmesor, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6562 -0.6564  0.2369  1.0148  2.5832 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -2.02499    0.70034  -2.891  0.00463 **
## group1       0.73695    0.30330   2.430  0.01674 * 
## actupmesor   0.18836    0.07911   2.381  0.01901 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.495 on 109 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.07228,    Adjusted R-squared:  0.05525 
## F-statistic: 4.246 on 2 and 109 DF,  p-value: 0.01676
summary(lm(ds_backward_score   ~ age + gender + group + actupmesor, data = d)) # NS
## 
## Call:
## lm(formula = ds_backward_score ~ age + gender + group + actupmesor, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0364 -1.8211 -0.2138  1.7493  6.1281 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.38834    1.83175   6.763 7.41e-10 ***
## age         -0.08822    0.04566  -1.932   0.0560 .  
## gender      -1.20614    0.51251  -2.353   0.0204 *  
## group1       4.24240    2.16383   1.961   0.0525 .  
## actupmesor   0.06607    0.13643   0.484   0.6292    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.556 on 107 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.0954, Adjusted R-squared:  0.06158 
## F-statistic: 2.821 on 4 and 107 DF,  p-value: 0.02858
summary(lm(cvlt_ldelay_recall_zscore  ~ actupmesor, data = d)) # NS
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ actupmesor, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.46732 -0.52470  0.05424  0.59888  2.07220 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.65169    0.52735   1.236    0.222
## actupmesor  -0.03076    0.07152  -0.430    0.669
## 
## Residual standard error: 1.022 on 57 degrees of freedom
##   (75 observations deleted due to missingness)
## Multiple R-squared:  0.003235,   Adjusted R-squared:  -0.01425 
## F-statistic: 0.185 on 1 and 57 DF,  p-value: 0.6687
summary(lm(trails_b_z_score ~ group + actdownmesor, data = d)) # p = 0.01735
## 
## Call:
## lm(formula = trails_b_z_score ~ group + actdownmesor, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3407 -0.5575  0.2192  0.9828  2.4270 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -5.5558     2.1315  -2.607  0.01042 * 
## group1         0.8984     0.3324   2.703  0.00797 **
## actdownmesor   0.2097     0.0868   2.416  0.01735 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.493 on 109 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.07364,    Adjusted R-squared:  0.05664 
## F-statistic: 4.333 on 2 and 109 DF,  p-value: 0.01547
summary(lm(ds_backward_score   ~ age + gender + group + actdownmesor, data = d)) # NS
## 
## Call:
## lm(formula = ds_backward_score ~ age + gender + group + actdownmesor, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1413 -1.8923 -0.2542  1.8468  6.1711 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  12.17292    3.87572   3.141  0.00218 **
## age          -0.09183    0.04493  -2.044  0.04341 * 
## gender       -1.19452    0.51438  -2.322  0.02211 * 
## group1        4.38746    2.15888   2.032  0.04460 * 
## actdownmesor  0.03419    0.14498   0.236  0.81400   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.558 on 107 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.09389,    Adjusted R-squared:  0.06001 
## F-statistic: 2.772 on 4 and 107 DF,  p-value: 0.03084
summary(lm(cvlt_ldelay_recall_zscore  ~ actdownmesor, data = d)) # NS
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ actdownmesor, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.44676 -0.44367  0.07019  0.57747  2.05556 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.572823   1.669842   0.343    0.733
## actdownmesor -0.006249   0.073974  -0.084    0.933
## 
## Residual standard error: 1.024 on 57 degrees of freedom
##   (75 observations deleted due to missingness)
## Multiple R-squared:  0.0001252,  Adjusted R-squared:  -0.01742 
## F-statistic: 0.007137 on 1 and 57 DF,  p-value: 0.933
#No significant relationships between cognition and sleep variables
summary(lm(trails_b_z_score ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = trails_b_z_score ~ group + sleep_time, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5746 -0.6442  0.2112  1.1066  2.2780 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.108467   0.475809   0.228   0.8201  
## group1       0.535849   0.278490   1.924   0.0567 .
## sleep_time  -0.001477   0.001264  -1.169   0.2448  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.519 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.03763,    Adjusted R-squared:  0.02132 
## F-statistic: 2.307 on 2 and 118 DF,  p-value: 0.1041
summary(lm(ds_backward_score   ~ group + sleep_time, data = d)) 
## 
## Call:
## lm(formula = ds_backward_score ~ group + sleep_time, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2777 -1.8491 -0.1473  1.8479  6.0894 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.714714   0.829041  11.718   <2e-16 ***
## group1      -0.284165   0.485302  -0.586    0.559    
## sleep_time  -0.001676   0.002198  -0.762    0.447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.643 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.008762,   Adjusted R-squared:  -0.008039 
## F-statistic: 0.5215 on 2 and 118 DF,  p-value: 0.595
summary(lm(cvlt_ldelay_recall_zscore  ~ sleep_time, data = d))
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.44978 -0.45051  0.06217  0.56969  2.02864 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2350424  0.4317497   0.544    0.588
## sleep_time  0.0005752  0.0011265   0.511    0.611
## 
## Residual standard error: 1.002 on 63 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.004121,   Adjusted R-squared:  -0.01169 
## F-statistic: 0.2607 on 1 and 63 DF,  p-value: 0.6114
summary(lm(trails_b_z_score ~ group + percent_wake, data = d)) 
## 
## Call:
## lm(formula = trails_b_z_score ~ group + percent_wake, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5416 -0.7038  0.2494  1.1092  2.4287 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -1.56899    1.21132  -1.295   0.1978  
## group1        0.54809    0.28167   1.946   0.0541 .
## percent_wake  0.02998    0.03051   0.982   0.3279  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.522 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.03438,    Adjusted R-squared:  0.01802 
## F-statistic: 2.101 on 2 and 118 DF,  p-value: 0.1269
summary(lm(ds_backward_score   ~ group + percent_wake, data = d)) 
## 
## Call:
## lm(formula = ds_backward_score ~ group + percent_wake, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0548 -1.9637 -0.2343  1.9179  6.2316 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   7.94440    2.10172   3.780 0.000248 ***
## group1       -0.27333    0.49146  -0.556 0.579157    
## percent_wake  0.03058    0.05287   0.578 0.564047    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.646 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.006695,   Adjusted R-squared:  -0.01014 
## F-statistic: 0.3977 on 2 and 118 DF,  p-value: 0.6728
summary(lm(cvlt_ldelay_recall_zscore  ~ percent_wake, data = d))
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ percent_wake, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.49062 -0.51902  0.02878  0.56434  1.92863 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   1.84389    0.96151   1.918   0.0597 .
## percent_wake -0.03735    0.02549  -1.466   0.1477  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9877 on 63 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.03297,    Adjusted R-squared:  0.01762 
## F-statistic: 2.148 on 1 and 63 DF,  p-value: 0.1477
summary(lm(trails_b_z_score ~ group + onset_latency, data = d))
## 
## Call:
## lm(formula = trails_b_z_score ~ group + onset_latency, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4709 -0.6393  0.2692  1.1102  2.3662 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -0.456679   0.259938  -1.757   0.0815 .
## group1         0.472736   0.286496   1.650   0.1016  
## onset_latency  0.002669   0.007127   0.374   0.7087  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.527 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.02764,    Adjusted R-squared:  0.01116 
## F-statistic: 1.677 on 2 and 118 DF,  p-value: 0.1913
summary(lm(ds_backward_score   ~  group + onset_latency, data = d))
## 
## Call:
## lm(formula = ds_backward_score ~ group + onset_latency, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2069 -1.8602 -0.1827  1.9115  6.2700 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    9.23365    0.45388  20.344   <2e-16 ***
## group1        -0.29095    0.49628  -0.586    0.559    
## onset_latency -0.00394    0.01233  -0.320    0.750    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.649 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.004739,   Adjusted R-squared:  -0.01213 
## F-statistic: 0.281 on 2 and 118 DF,  p-value: 0.7556
summary(lm(cvlt_ldelay_recall_zscore  ~ onset_latency, data = d))
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ onset_latency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.45392 -0.46068  0.05358  0.56099  2.04000 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.4679592  0.2104112   2.224   0.0297 *
## onset_latency -0.0006749  0.0052484  -0.129   0.8981  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.004 on 63 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.0002624,  Adjusted R-squared:  -0.01561 
## F-statistic: 0.01654 on 1 and 63 DF,  p-value: 0.8981
summary(lm(trails_b_z_score ~ group + efficiency, data = d)) 
## 
## Call:
## lm(formula = trails_b_z_score ~ group + efficiency, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5271 -0.6562  0.1934  1.1011  2.3801 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.481437   1.465896  -0.328   0.7432  
## group1       0.499804   0.279068   1.791   0.0759 .
## efficiency   0.001189   0.020103   0.059   0.9529  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.528 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.02651,    Adjusted R-squared:  0.01001 
## F-statistic: 1.607 on 2 and 118 DF,  p-value: 0.2049
summary(lm(ds_backward_score   ~ group + efficiency, data = d)) 
## 
## Call:
## lm(formula = ds_backward_score ~ group + efficiency, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2006 -1.8251 -0.1607  1.8624  6.2539 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.60719    2.53775   3.392 0.000946 ***
## group1      -0.32033    0.48422  -0.662 0.509558    
## efficiency   0.00742    0.03481   0.213 0.831567    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.649 on 118 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.004261,   Adjusted R-squared:  -0.01262 
## F-statistic: 0.2525 on 2 and 118 DF,  p-value: 0.7773
summary(lm(cvlt_ldelay_recall_zscore  ~ efficiency, data = d)) 
## 
## Call:
## lm(formula = cvlt_ldelay_recall_zscore ~ efficiency, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4653 -0.5267  0.0100  0.5654  1.9369 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.54085    1.15174  -0.470    0.640
## efficiency   0.01386    0.01608   0.862    0.392
## 
## Residual standard error: 0.9986 on 63 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.01166,    Adjusted R-squared:  -0.004032 
## F-statistic: 0.743 on 1 and 63 DF,  p-value: 0.392
d2.mlt <- melt(select(d, record_id, group, IS:actquot, trails_b_z_score, -rsqact, -fnlrgact, -L5_starttime, -M10_starttime, -actbeta, -actwidthratio), id.vars=c('record_id', 'group', 'trails_b_z_score'))

ggplot(data = d2.mlt) + 
  ggtitle('Circadian Measures and TMT-B Performance') + 
  geom_point(aes(x = value, y = trails_b_z_score, group = group, color = group), size=0.5) + 
  stat_smooth(aes(x = value, y = trails_b_z_score, group = group, color = group), method = 'loess', se = FALSE, fullrange = FALSE) +
  facet_wrap(~ variable,  scales='free') + 
  theme_minimal() +
  scale_color_manual(values = brewer.pal(8, "Paired")[7:8], name = 'Group', labels = c('Young Adults', 'Older Adults')) + 
  xlab(element_blank()) + ylab('TMT-B') +
  theme(axis.text.y = element_blank(), axis.text.x = element_blank())
## Warning: Removed 303 rows containing non-finite values (stat_smooth).
## Warning: Removed 303 rows containing missing values (geom_point).

summary(d$actalph)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -1.0000 -0.5764 -0.4825 -0.4631 -0.3996  0.9533      17
lm1 <- lm(trails_b_z_score ~ group + fact, data = d)
par(mfrow=c(2,2))
plot(lm1, which=1:4)

shapiro.test(lm1$residuals) #residuals not normally distributed
## 
##  Shapiro-Wilk normality test
## 
## data:  lm1$residuals
## W = 0.92007, p-value = 4.849e-06
lm2 <- lm(trails_b_z_score ~ group + actupmesor, data = d)
par(mfrow=c(2,2))
plot(lm2, which=1:4)

shapiro.test(lm2$residuals) #residuals not normally distributed
## 
##  Shapiro-Wilk normality test
## 
## data:  lm2$residuals
## W = 0.90872, p-value = 1.177e-06
trails_data <- dplyr::select(d, wb_clustering_x:fpn_betweenness_x, contains('trail'))
corrgram::corrgram(trails_data, cor.method = 'spearman', lower.panel=corrgram::panel.fill, upper.panel=corrgram::panel.cor)

ds_data <- dplyr::select(d, wb_clustering_x:fpn_betweenness_x, starts_with('ds'))
corrgram::corrgram(ds_data, cor.method = 'spearman', lower.panel=corrgram::panel.fill,  upper.panel=corrgram::panel.cor)

cvlt_data <- dplyr::select(d, wb_clustering_x:fpn_betweenness_x, contains('cvlt_z'))
corrgram::corrgram(cvlt_data, cor.method = 'spearman', lower.panel=corrgram::panel.fill, upper.panel=corrgram::panel.cor)

cowat_data <- dplyr::select(d, wb_clustering_x:fpn_betweenness_x, contains('cowat'))
corrgram::corrgram(cowat_data, cor.method = 'spearman', lower.panel=corrgram::panel.fill, upper.panel=corrgram::panel.cor)

stroop_data <- dplyr::select(d, wb_clustering_x:fpn_betweenness_x, contains('stroop'))
corrgram::corrgram(stroop_data, cor.method = 'spearman', lower.panel=corrgram::panel.fill, upper.panel=corrgram::panel.cor)

d3.mlt <- melt(select(d, record_id, group, cvlt_ldelay_recall_zscore, wb_clustering_x:fpn_betweenness_x), id.vars=c('record_id', 'group', 'cvlt_ldelay_recall_zscore'))

ggplot(data = d3.mlt) + 
  ggtitle('BCT Metrics and CVLT Long Delay z-score') + 
  geom_point(aes(x = value, y = cvlt_ldelay_recall_zscore, group = group, color = group), size=0.5) + 
  #stat_smooth(aes(x = value, y = ds_backward_score, group = group, color = group), method = 'loess', se = FALSE, fullrange = FALSE) +
  facet_wrap(~ variable,  scales='free') + 
  theme_minimal() +
  scale_color_manual(values = brewer.pal(8, "Paired")[7:8], name = 'Group', labels = c('Young Adults', 'Older Adults')) + 
  xlab(element_blank()) + ylab('CVLT') +
  theme(axis.text.y = element_blank(), axis.text.x = element_blank())
## Warning: Removed 1455 rows containing missing values (geom_point).

summary(lm(dmn_modularity_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.112104 -0.038123  0.001876  0.035323  0.146968 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.36208    0.03654   9.908 5.86e-15 ***
## group1      -0.05246    0.04968  -1.056    0.295    
## IS          -0.02885    0.08894  -0.324    0.747    
## group1:IS    0.06033    0.11218   0.538    0.592    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05738 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05577,    Adjusted R-squared:  0.0153 
## F-statistic: 1.378 on 3 and 70 DF,  p-value: 0.2566
summary(lm(dmn_efficiency_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * IS, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7431 -0.2053 -0.0256  0.2124  1.0236 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.1774     0.2218  14.324   <2e-16 ***
## group1       -0.5075     0.3016  -1.683   0.0969 .  
## IS            0.2479     0.5399   0.459   0.6476    
## group1:IS     0.2004     0.6809   0.294   0.7694    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3483 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.2633, Adjusted R-squared:  0.2317 
## F-statistic:  8.34 on 3 and 70 DF,  p-value: 8.135e-05
summary(lm(dmn_clustering_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.062003 -0.021856 -0.003238  0.019953  0.074253 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.27165    0.01870  14.526   <2e-16 ***
## group1       0.02612    0.02542   1.028    0.308    
## IS           0.05895    0.04551   1.295    0.200    
## group1:IS   -0.07742    0.05740  -1.349    0.182    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02936 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.03757,    Adjusted R-squared:  -0.003675 
## F-statistic: 0.9109 on 3 and 70 DF,  p-value: 0.4403
summary(lm(dmn_participation_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.183718 -0.037915  0.002937  0.045423  0.152378 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.45341    0.04250  10.668 2.54e-16 ***
## group1      -0.06873    0.05779  -1.189    0.238    
## IS           0.07242    0.10345   0.700    0.486    
## group1:IS    0.09696    0.13047   0.743    0.460    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06674 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.083,  Adjusted R-squared:  0.0437 
## F-statistic: 2.112 on 3 and 70 DF,  p-value: 0.1065
summary(lm(dmn_modularity_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.109905 -0.039188  0.001182  0.034580  0.143812 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.346982   0.023285  14.902   <2e-16 ***
## group1      -0.026814   0.013886  -1.931   0.0575 .  
## IS           0.009077   0.053929   0.168   0.8668    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0571 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05187,    Adjusted R-squared:  0.02516 
## F-statistic: 1.942 on 2 and 71 DF,  p-value: 0.1509
summary(lm(dmn_efficiency_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + IS, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73703 -0.20557 -0.01522  0.22092  1.01080 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.12728    0.14114  22.158  < 2e-16 ***
## group1      -0.42231    0.08417  -5.017 3.73e-06 ***
## IS           0.37389    0.32688   1.144    0.257    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3461 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.2624, Adjusted R-squared:  0.2416 
## F-statistic: 12.63 on 2 and 71 DF,  p-value: 2.031e-05
summary(lm(dmn_clustering_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.064339 -0.022741 -0.002518  0.019037  0.076297 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.291018   0.012044  24.162   <2e-16 ***
## group1      -0.006785   0.007183  -0.945    0.348    
## IS           0.010279   0.027895   0.368    0.714    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02953 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01256,    Adjusted R-squared:  -0.01525 
## F-statistic: 0.4517 on 2 and 71 DF,  p-value: 0.6384
summary(lm(dmn_participation_x ~ group + IS, data = d)) # *
## 
## Call:
## lm(formula = dmn_participation_x ~ group + IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.180793 -0.038800  0.007265  0.049041  0.144545 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.42915    0.02713  15.817   <2e-16 ***
## group1      -0.02752    0.01618  -1.701   0.0934 .  
## IS           0.13338    0.06284   2.122   0.0373 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06653 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.07576,    Adjusted R-squared:  0.04973 
## F-statistic:  2.91 on 2 and 71 DF,  p-value: 0.061
#---------------------------------------------------------

summary(lm(dmn_modularity_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.122659 -0.038845 -0.000611  0.035899  0.145679 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.30827    0.03493   8.824 5.55e-13 ***
## group1       0.03044    0.04882   0.623    0.535    
## IV           0.04623    0.03688   1.254    0.214    
## group1:IV   -0.06281    0.05294  -1.186    0.239    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0568 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.07478,    Adjusted R-squared:  0.03513 
## F-statistic: 1.886 on 3 and 70 DF,  p-value: 0.1399
summary(lm(dmn_efficiency_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.80823 -0.23404 -0.00458  0.22425  1.04151 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.4677     0.2116  16.389   <2e-16 ***
## group1       -0.2873     0.2957  -0.972    0.334    
## IV           -0.2093     0.2233  -0.937    0.352    
## group1:IV    -0.1386     0.3207  -0.432    0.667    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.344 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.2813, Adjusted R-squared:  0.2505 
## F-statistic: 9.133 on 3 and 70 DF,  p-value: 3.527e-05
summary(lm(dmn_clustering_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.053654 -0.020411 -0.003014  0.019713  0.080206 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.3171421  0.0179053  17.712   <2e-16 ***
## group1      -0.0070203  0.0250216  -0.281    0.780    
## IV          -0.0240665  0.0189005  -1.273    0.207    
## group1:IV   -0.0003998  0.0271348  -0.015    0.988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02911 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05393,    Adjusted R-squared:  0.01339 
## F-statistic:  1.33 on 3 and 70 DF,  p-value: 0.2715
summary(lm(dmn_participation_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group * IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.189670 -0.038737  0.004867  0.048461  0.132920 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.471943   0.042475  11.111   <2e-16 ***
## group1      -0.010548   0.059356  -0.178    0.859    
## IV           0.011244   0.044835   0.251    0.803    
## group1:IV   -0.007706   0.064369  -0.120    0.905    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06906 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01809,    Adjusted R-squared:  -0.024 
## F-statistic: 0.4298 on 3 and 70 DF,  p-value: 0.7323
summary(lm(dmn_modularity_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.114598 -0.037195  0.002641  0.035811  0.148695 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.33617    0.02591  12.975   <2e-16 ***
## group1      -0.02528    0.01337  -1.891   0.0627 .  
## IV           0.01575    0.02654   0.594   0.5546    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05697 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05618,    Adjusted R-squared:  0.02959 
## F-statistic: 2.113 on 2 and 71 DF,  p-value: 0.1284
summary(lm(dmn_efficiency_x ~ group + IV, data = d)) #
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.77395 -0.23134  0.00052  0.21991  1.03912 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.52929    0.15558  22.685  < 2e-16 ***
## group1      -0.41033    0.08028  -5.112 2.59e-06 ***
## IV          -0.27657    0.15934  -1.736   0.0869 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3421 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.2794, Adjusted R-squared:  0.2591 
## F-statistic: 13.76 on 2 and 71 DF,  p-value: 8.881e-06
summary(lm(dmn_clustering_x ~ group + IV, data = d)) #
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.05356 -0.02046 -0.00302  0.01959  0.08023 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.317320   0.013148  24.135   <2e-16 ***
## group1      -0.007375   0.006784  -1.087   0.2807    
## IV          -0.024260   0.013466  -1.802   0.0758 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02891 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05393,    Adjusted R-squared:  0.02728 
## F-statistic: 2.024 on 2 and 71 DF,  p-value: 0.1397
summary(lm(dmn_participation_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group + IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.191375 -0.037965  0.005543  0.047914  0.134656 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.475366   0.031192  15.240   <2e-16 ***
## group1      -0.017383   0.016095  -1.080    0.284    
## IV           0.007506   0.031946   0.235    0.815    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06858 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01788,    Adjusted R-squared:  -0.009781 
## F-statistic: 0.6464 on 2 and 71 DF,  p-value: 0.527
#---------------------------------------------------------

summary(lm(dmn_modularity_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.111575 -0.037934 -0.003595  0.033827  0.151931 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.344486   0.051472   6.693 4.52e-09 ***
## group1       0.036302   0.076770   0.473    0.638    
## RA           0.007274   0.060345   0.121    0.904    
## group1:RA   -0.074565   0.090176  -0.827    0.411    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0571 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.06515,    Adjusted R-squared:  0.02509 
## F-statistic: 1.626 on 3 and 70 DF,  p-value: 0.1911
summary(lm(dmn_efficiency_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76111 -0.19850 -0.02646  0.22946  1.06958 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.44902    0.31613  10.910   <2e-16 ***
## group1      -0.45242    0.47150  -0.960    0.341    
## RA          -0.20593    0.37062  -0.556    0.580    
## group1:RA    0.06791    0.55384   0.123    0.903    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3507 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.2533, Adjusted R-squared:  0.2213 
## F-statistic: 7.915 on 3 and 70 DF,  p-value: 0.0001282
summary(lm(dmn_clustering_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.065233 -0.022471 -0.001541  0.019663  0.076597 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.306413   0.026781  11.441   <2e-16 ***
## group1      -0.007264   0.039943  -0.182    0.856    
## RA          -0.013461   0.031397  -0.429    0.669    
## group1:RA    0.001428   0.046918   0.030    0.976    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02971 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01494,    Adjusted R-squared:  -0.02728 
## F-statistic: 0.3538 on 3 and 70 DF,  p-value: 0.7865
summary(lm(dmn_participation_x ~ group * RA, data = d)) # **
## 
## Call:
## lm(formula = dmn_participation_x ~ group * RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.181254 -0.041710  0.005337  0.043609  0.139249 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.45101    0.05535   8.148 9.77e-12 ***
## group1      -0.24477    0.08256  -2.965  0.00414 ** 
## RA           0.03719    0.06490   0.573  0.56848    
## group1:RA    0.27117    0.09698   2.796  0.00667 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06141 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.2238, Adjusted R-squared:  0.1905 
## F-statistic: 6.727 on 3 and 70 DF,  p-value: 0.0004709
summary(lm(dmn_modularity_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.109486 -0.038923  0.001191  0.033243  0.148117 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.37253    0.03864   9.642 1.55e-14 ***
## group1      -0.02621    0.01329  -1.973   0.0524 .  
## RA          -0.02612    0.04474  -0.584   0.5613    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05697 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05602,    Adjusted R-squared:  0.02943 
## F-statistic: 2.107 on 2 and 71 DF,  p-value: 0.1292
summary(lm(dmn_efficiency_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76020 -0.19975 -0.02175  0.23637  1.06653 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.42348    0.23617  14.496  < 2e-16 ***
## group1      -0.39548    0.08123  -4.868 6.58e-06 ***
## RA          -0.17552    0.27349  -0.642    0.523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3482 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.2531, Adjusted R-squared:  0.2321 
## F-statistic: 12.03 on 2 and 71 DF,  p-value: 3.163e-05
summary(lm(dmn_clustering_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.065214 -0.022464 -0.001523  0.019681  0.076604 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.305876   0.020005  15.290   <2e-16 ***
## group1      -0.006066   0.006881  -0.882    0.381    
## RA          -0.012822   0.023166  -0.553    0.582    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0295 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01493,    Adjusted R-squared:  -0.01282 
## F-statistic: 0.5379 on 2 and 71 DF,  p-value: 0.5863
#---------------------------------------------------------
summary(lm(dmn_modularity_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.104574 -0.042681  0.002067  0.038124  0.156193 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.669e-01  2.221e-02  16.515   <2e-16 ***
## group1      -6.133e-02  3.186e-02  -1.925   0.0583 .  
## fact        -3.996e-06  4.992e-06  -0.800   0.4262    
## group1:fact  8.813e-06  6.834e-06   1.290   0.2015    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05644 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.06649,    Adjusted R-squared:  0.02591 
## F-statistic: 1.638 on 3 and 69 DF,  p-value: 0.1885
summary(lm(dmn_efficiency_x ~ group * fact, data = d)) # *
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.79630 -0.17767 -0.05797  0.19886  1.11169 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.395e+00  1.306e-01  25.996  < 2e-16 ***
## group1      -8.170e-01  1.873e-01  -4.362 4.42e-05 ***
## fact        -2.929e-05  2.935e-05  -0.998   0.3217    
## group1:fact  1.009e-04  4.018e-05   2.510   0.0144 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3318 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.3148, Adjusted R-squared:  0.285 
## F-statistic: 10.57 on 3 and 69 DF,  p-value: 8.37e-06
summary(lm(dmn_clustering_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.062777 -0.021476 -0.003239  0.021375  0.078796 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.892e-01  1.160e-02  24.939   <2e-16 ***
## group1      -1.603e-02  1.663e-02  -0.964    0.338    
## fact         1.457e-06  2.605e-06   0.559    0.578    
## group1:fact  2.149e-06  3.567e-06   0.602    0.549    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02946 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.04518,    Adjusted R-squared:  0.003662 
## F-statistic: 1.088 on 3 and 69 DF,  p-value: 0.36
summary(lm(dmn_participation_x ~ group * fact, data = d)) # *
## 
## Call:
## lm(formula = dmn_participation_x ~ group * fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.192262 -0.044581  0.005732  0.045846  0.131849 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.004e-01  2.590e-02  19.324   <2e-16 ***
## group1      -9.253e-02  3.714e-02  -2.492   0.0151 *  
## fact        -4.461e-06  5.819e-06  -0.767   0.4459    
## group1:fact  1.654e-05  7.967e-06   2.076   0.0416 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06579 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.09638,    Adjusted R-squared:  0.05709 
## F-statistic: 2.453 on 3 and 69 DF,  p-value: 0.07056
summary(lm(dmn_modularity_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.111465 -0.036796  0.005418  0.034051  0.142505 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.477e-01  1.659e-02  20.957   <2e-16 ***
## group1      -2.402e-02  1.339e-02  -1.794   0.0771 .  
## fact         7.057e-07  3.426e-06   0.206   0.8374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0567 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.044,  Adjusted R-squared:  0.01668 
## F-statistic: 1.611 on 2 and 70 DF,  p-value: 0.2071
summary(lm(dmn_clustering_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.061159 -0.021287 -0.002668  0.023603  0.080422 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.845e-01  8.580e-03  33.158   <2e-16 ***
## group1      -6.929e-03  6.922e-03  -1.001    0.320    
## fact         2.604e-06  1.771e-06   1.470    0.146    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02932 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.04015,    Adjusted R-squared:  0.01273 
## F-statistic: 1.464 on 2 and 70 DF,  p-value: 0.2383
#---------------------------------------------------------

summary(lm(dmn_modularity_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.116078 -0.040402  0.005576  0.031654  0.148714 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.39803    0.06091   6.535 9.17e-09 ***
## group1        -0.04426    0.08158  -0.543    0.589    
## actamp        -0.03034    0.03853  -0.787    0.434    
## group1:actamp  0.01251    0.05225   0.239    0.812    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05677 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.0554, Adjusted R-squared:  0.01433 
## F-statistic: 1.349 on 3 and 69 DF,  p-value: 0.2658
summary(lm(dmn_efficiency_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.80199 -0.20771 -0.01272  0.21313  1.07048 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     3.3762     0.3692   9.146 1.62e-13 ***
## group1         -0.9770     0.4944  -1.976   0.0522 .  
## actamp         -0.0640     0.2335  -0.274   0.7848    
## group1:actamp   0.3916     0.3167   1.237   0.2204    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3441 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.2633, Adjusted R-squared:  0.2312 
## F-statistic: 8.219 on 3 and 69 DF,  p-value: 9.434e-05
summary(lm(dmn_clustering_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.063632 -0.018714 -0.003235  0.018436  0.083204 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.233009   0.030543   7.629 9.53e-11 ***
## group1         0.007201   0.040907   0.176   0.8608    
## actamp         0.039716   0.019320   2.056   0.0436 *  
## group1:actamp -0.007399   0.026202  -0.282   0.7785    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02847 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1082, Adjusted R-squared:  0.06946 
## F-statistic: 2.791 on 3 and 69 DF,  p-value: 0.04684
summary(lm(dmn_participation_x ~ group * actamp, data = d)) # 
## 
## Call:
## lm(formula = dmn_participation_x ~ group * actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.172837 -0.044346  0.002105  0.039644  0.136660 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.55894    0.07070   7.905 2.98e-11 ***
## group1        -0.22254    0.09470  -2.350   0.0216 *  
## actamp        -0.04906    0.04473  -1.097   0.2765    
## group1:actamp  0.13162    0.06066   2.170   0.0335 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0659 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.09334,    Adjusted R-squared:  0.05392 
## F-statistic: 2.368 on 3 and 69 DF,  p-value: 0.07823
summary(lm(dmn_modularity_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.116987 -0.039542  0.002837  0.033169  0.150441 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.38740    0.04139   9.359 5.82e-14 ***
## group1      -0.02500    0.01333  -1.875   0.0649 .  
## actamp      -0.02354    0.02585  -0.910   0.3657    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05639 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.05461,    Adjusted R-squared:  0.0276 
## F-statistic: 2.022 on 2 and 70 DF,  p-value: 0.1401
summary(lm(dmn_efficiency_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.75048 -0.18510 -0.00085  0.24441  0.99880 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.04322    0.25353  12.003  < 2e-16 ***
## group1      -0.37385    0.08164  -4.579 1.97e-05 ***
## actamp       0.14893    0.15834   0.941     0.35    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3454 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.2469, Adjusted R-squared:  0.2254 
## F-statistic: 11.48 on 2 and 70 DF,  p-value: 4.888e-05
summary(lm(dmn_clustering_x ~ group + actamp, data = d)) # **
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.063754 -0.018802 -0.004377  0.020229  0.082548 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.239299   0.020759  11.527  < 2e-16 ***
## group1      -0.004193   0.006684  -0.627  0.53249    
## actamp       0.035694   0.012965   2.753  0.00751 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02828 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1072, Adjusted R-squared:  0.08169 
## F-statistic: 4.203 on 2 and 70 DF,  p-value: 0.0189
#---------------------------------------------------------

summary(lm(dmn_modularity_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.109958 -0.037680 -0.005094  0.033524  0.151591 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.3440711  0.1007758   3.414  0.00107 **
## group1         0.1523493  0.1402975   1.086  0.28130   
## actphi         0.0003975  0.0061179   0.065  0.94838   
## group1:actphi -0.0117903  0.0089439  -1.318  0.19178   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05591 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.08396,    Adjusted R-squared:  0.04413 
## F-statistic: 2.108 on 3 and 69 DF,  p-value: 0.1071
summary(lm(dmn_efficiency_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * actphi, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73699 -0.20639 -0.00919  0.22304  1.04486 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    3.395275   0.628212   5.405 8.74e-07 ***
## group1        -0.038780   0.874581  -0.044    0.965    
## actphi        -0.007264   0.038138  -0.190    0.850    
## group1:actphi -0.023769   0.055754  -0.426    0.671    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3485 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.2442, Adjusted R-squared:  0.2113 
## F-statistic: 7.431 on 3 and 69 DF,  p-value: 0.0002202
summary(lm(dmn_clustering_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.064725 -0.024070 -0.002608  0.016080  0.076650 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    2.958e-01  5.387e-02   5.491 6.22e-07 ***
## group1        -4.250e-02  7.499e-02  -0.567    0.573    
## actphi        -4.328e-05  3.270e-03  -0.013    0.989    
## group1:actphi  2.444e-03  4.781e-03   0.511    0.611    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02988 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.01728,    Adjusted R-squared:  -0.02544 
## F-statistic: 0.4045 on 3 and 69 DF,  p-value: 0.7502
summary(lm(dmn_participation_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.184131 -0.046960  0.005961  0.046840  0.143427 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.517250   0.120893   4.279 5.94e-05 ***
## group1        -0.244626   0.168304  -1.453    0.151    
## actphi        -0.002134   0.007339  -0.291    0.772    
## group1:actphi  0.014802   0.010729   1.380    0.172    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06707 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.06099,    Adjusted R-squared:  0.02016 
## F-statistic: 1.494 on 3 and 69 DF,  p-value: 0.2239
summary(lm(dmn_modularity_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.116517 -0.035314 -0.003438  0.036331  0.147037 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.434594   0.074147   5.861 1.37e-07 ***
## group1      -0.031566   0.014876  -2.122   0.0374 *  
## actphi      -0.005119   0.004486  -1.141   0.2577    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0562 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.06089,    Adjusted R-squared:  0.03405 
## F-statistic: 2.269 on 2 and 70 DF,  p-value: 0.111
summary(lm(dmn_efficiency_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + actphi, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.70945 -0.21270 -0.00609  0.22153  1.03863 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.57776    0.45710   7.827 3.80e-11 ***
## group1      -0.40955    0.09171  -4.466 2.99e-05 ***
## actphi      -0.01839    0.02766  -0.665    0.508    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3465 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.2422, Adjusted R-squared:  0.2206 
## F-statistic: 11.19 on 2 and 70 DF,  p-value: 6.086e-05
summary(lm(dmn_clustering_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.067558 -0.024533 -0.002747  0.017797  0.078722 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.277053   0.039219   7.064 9.53e-10 ***
## group1      -0.004368   0.007868  -0.555    0.581    
## actphi       0.001100   0.002373   0.464    0.644    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02973 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.01356,    Adjusted R-squared:  -0.01462 
## F-statistic: 0.4812 on 2 and 70 DF,  p-value: 0.6201
summary(lm(dmn_participation_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group + actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.194075 -0.041461  0.001791  0.054278  0.126275 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.403605   0.089052   4.532 2.35e-05 ***
## group1      -0.013732   0.017866  -0.769    0.445    
## actphi       0.004792   0.005388   0.889    0.377    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0675 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.03509,    Adjusted R-squared:  0.007518 
## F-statistic: 1.273 on 2 and 70 DF,  p-value: 0.2865
#---------------------------------------------------------
summary(lm(dmn_modularity_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.108462 -0.040859  0.000418  0.033036  0.148570 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.2425486  0.0607980   3.989 0.000159 ***
## group1             0.1008379  0.0816104   1.236 0.220679    
## sleep_time         0.0003288  0.0001831   1.796 0.076690 .  
## group1:sleep_time -0.0003868  0.0002377  -1.627 0.108176    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05613 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1003, Adjusted R-squared:  0.06225 
## F-statistic: 2.637 on 3 and 71 DF,  p-value: 0.0562
summary(lm(dmn_efficiency_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74259 -0.20371 -0.02172  0.21182  0.99417 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        3.638247   0.375792   9.682 1.31e-14 ***
## group1            -0.798817   0.504433  -1.584    0.118    
## sleep_time        -0.001102   0.001131  -0.974    0.333    
## group1:sleep_time  0.001216   0.001469   0.828    0.411    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3469 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.263,  Adjusted R-squared:  0.2318 
## F-statistic: 8.444 on 3 and 71 DF,  p-value: 7.141e-05
summary(lm(dmn_clustering_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.063239 -0.023123 -0.000635  0.018605  0.078777 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        3.417e-01  3.150e-02  10.847   <2e-16 ***
## group1            -4.293e-02  4.229e-02  -1.015    0.313    
## sleep_time        -1.418e-04  9.485e-05  -1.495    0.139    
## group1:sleep_time  1.145e-04  1.232e-04   0.930    0.356    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02908 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.04245,    Adjusted R-squared:  0.001988 
## F-statistic: 1.049 on 3 and 71 DF,  p-value: 0.3763
summary(lm(dmn_participation_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group * sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.170259 -0.039553  0.005276  0.041173  0.135262 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        3.980e-01  7.247e-02   5.493 5.82e-07 ***
## group1            -4.829e-02  9.727e-02  -0.496    0.621    
## sleep_time         2.563e-04  2.182e-04   1.175    0.244    
## group1:sleep_time  7.382e-05  2.833e-04   0.261    0.795    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0669 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07502,    Adjusted R-squared:  0.03594 
## F-statistic:  1.92 on 3 and 71 DF,  p-value: 0.1342
summary(lm(dmn_modularity_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.115869 -0.038203  0.000163  0.035072  0.152369 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.179e-01  3.983e-02   7.981 1.66e-11 ***
## group1      -3.016e-02  1.347e-02  -2.240   0.0282 *  
## sleep_time   9.949e-05  1.181e-04   0.842   0.4024    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05677 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06672,    Adjusted R-squared:  0.0408 
## F-statistic: 2.574 on 2 and 72 DF,  p-value: 0.08325
summary(lm(dmn_efficiency_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.75070 -0.20422 -0.00207  0.23490  1.02702 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.4012965  0.2429005  14.003  < 2e-16 ***
## group1      -0.3869065  0.0821132  -4.712 1.16e-05 ***
## sleep_time  -0.0003811  0.0007203  -0.529    0.598    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3462 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2559, Adjusted R-squared:  0.2352 
## F-statistic: 12.38 on 2 and 72 DF,  p-value: 2.397e-05
summary(lm(dmn_clustering_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.06400 -0.02197 -0.00142  0.01926  0.07780 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.194e-01  2.039e-02  15.666   <2e-16 ***
## group1      -4.145e-03  6.892e-03  -0.601    0.549    
## sleep_time  -7.391e-05  6.045e-05  -1.223    0.225    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02906 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.03079,    Adjusted R-squared:  0.003866 
## F-statistic: 1.144 on 2 and 72 DF,  p-value: 0.3244
summary(lm(dmn_participation_x ~ group + sleep_time, data = d)) # *
## 
## Call:
## lm(formula = dmn_participation_x ~ group + sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.172068 -0.038697  0.005079  0.040363  0.137369 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.3836351  0.0466374   8.226 5.81e-12 ***
## group1      -0.0232918  0.0157659  -1.477   0.1439    
## sleep_time   0.0003001  0.0001383   2.170   0.0333 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06647 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07414,    Adjusted R-squared:  0.04842 
## F-statistic: 2.883 on 2 and 72 DF,  p-value: 0.06247
#---------------------------------------------------------
summary(lm(dmn_modularity_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.111625 -0.039788  0.000747  0.033809  0.154515 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.3342463  0.1057566   3.161  0.00232 **
## group1            -0.0855977  0.1450541  -0.590  0.55699   
## efficiency         0.0002279  0.0014692   0.155  0.87714   
## group1:efficiency  0.0008010  0.0020081   0.399  0.69119   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05721 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06528,    Adjusted R-squared:  0.02578 
## F-statistic: 1.653 on 3 and 71 DF,  p-value: 0.185
summary(lm(dmn_efficiency_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74316 -0.21623 -0.03005  0.13530  1.11008 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        4.21782    0.63486   6.644 5.25e-09 ***
## group1            -1.61726    0.87076  -1.857   0.0674 .  
## efficiency        -0.01313    0.00882  -1.489   0.1410    
## group1:efficiency  0.01700    0.01205   1.410   0.1629    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3434 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2778, Adjusted R-squared:  0.2472 
## F-statistic: 9.102 on 3 and 71 DF,  p-value: 3.564e-05
summary(lm(dmn_clustering_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.064250 -0.022226 -0.001156  0.018199  0.074228 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.3415037  0.0543525   6.283 2.35e-08 ***
## group1            -0.0428591  0.0745490  -0.575    0.567    
## efficiency        -0.0006469  0.0007551  -0.857    0.394    
## group1:efficiency  0.0005150  0.0010320   0.499    0.619    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0294 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.02127,    Adjusted R-squared:  -0.02008 
## F-statistic: 0.5144 on 3 and 71 DF,  p-value: 0.6737
summary(lm(dmn_participation_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group * efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.187120 -0.046843  0.007178  0.046813  0.138189 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        4.860e-01  1.234e-01   3.939 0.000189 ***
## group1            -2.798e-01  1.692e-01  -1.654 0.102634    
## efficiency        -5.202e-05  1.714e-03  -0.030 0.975868    
## group1:efficiency  3.657e-03  2.342e-03   1.561 0.122862    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06673 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07968,    Adjusted R-squared:  0.04079 
## F-statistic: 2.049 on 3 and 71 DF,  p-value: 0.1147
summary(lm(dmn_modularity_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.113871 -0.039754  0.000017  0.034424  0.150733 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.3034970  0.0719736   4.217  7.1e-05 ***
## group1      -0.0279818  0.0131724  -2.124   0.0371 *  
## efficiency   0.0006567  0.0009957   0.660   0.5117    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05688 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06319,    Adjusted R-squared:  0.03716 
## F-statistic: 2.428 on 2 and 72 DF,  p-value: 0.0954
summary(lm(dmn_efficiency_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.75135 -0.19980 -0.02557  0.20939  1.06771 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.565273   0.437577   8.148 8.12e-12 ***
## group1      -0.394555   0.080084  -4.927 5.16e-06 ***
## efficiency  -0.004032   0.006053  -0.666    0.507    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3458 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2575, Adjusted R-squared:  0.2369 
## F-statistic: 12.49 on 2 and 72 DF,  p-value: 2.21e-05
summary(lm(dmn_clustering_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.064498 -0.022706 -0.000786  0.018243  0.075293 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.3217344  0.0370134   8.692 7.82e-13 ***
## group1      -0.0058167  0.0067741  -0.859    0.393    
## efficiency  -0.0003712  0.0005120  -0.725    0.471    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02925 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.01784,    Adjusted R-squared:  -0.009443 
## F-statistic: 0.6539 on 2 and 72 DF,  p-value: 0.5231
summary(lm(dmn_participation_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group + efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.189923 -0.045773  0.006858  0.046630  0.136427 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.345559   0.085287   4.052 0.000127 ***
## group1      -0.016687   0.015609  -1.069 0.288623    
## efficiency   0.001906   0.001180   1.615 0.110633    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0674 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.04808,    Adjusted R-squared:  0.02164 
## F-statistic: 1.818 on 2 and 72 DF,  p-value: 0.1697
#---------------------------------------------------------
summary(lm(dmn_modularity_x ~ group * total_ac, data = d)) #
## 
## Call:
## lm(formula = dmn_modularity_x ~ group * total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.111650 -0.038074 -0.009147  0.039101  0.148045 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      3.438e-01  2.194e-02  15.671   <2e-16 ***
## group1          -8.804e-02  3.883e-02  -2.267   0.0264 *  
## total_ac         4.486e-08  1.336e-07   0.336   0.7379    
## group1:total_ac  4.774e-07  2.733e-07   1.747   0.0850 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05556 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1185, Adjusted R-squared:  0.08126 
## F-statistic: 3.182 on 3 and 71 DF,  p-value: 0.02906
summary(lm(dmn_efficiency_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group * total_ac, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7231 -0.2102  0.0345  0.1944  1.0333 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      3.144e+00  1.347e-01  23.336   <2e-16 ***
## group1          -5.484e-01  2.385e-01  -2.300   0.0244 *  
## total_ac         8.746e-07  8.202e-07   1.066   0.2899    
## group1:total_ac  1.333e-06  1.678e-06   0.795   0.4295    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3412 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2872, Adjusted R-squared:  0.2571 
## F-statistic: 9.536 on 3 and 71 DF,  p-value: 2.267e-05
summary(lm(dmn_clustering_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = dmn_clustering_x ~ group * total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.061514 -0.019534 -0.002643  0.015632  0.080344 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      2.814e-01  1.125e-02  25.017   <2e-16 ***
## group1          -2.256e-02  1.991e-02  -1.133    0.261    
## total_ac         9.109e-08  6.847e-08   1.330    0.188    
## group1:total_ac  1.446e-07  1.401e-07   1.032    0.305    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02848 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.08167,    Adjusted R-squared:  0.04287 
## F-statistic: 2.105 on 3 and 71 DF,  p-value: 0.1072
summary(lm(dmn_participation_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group * total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.190218 -0.039904  0.003421  0.047881  0.141558 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      4.681e-01  2.703e-02  17.319   <2e-16 ***
## group1           3.650e-02  4.784e-02   0.763    0.448    
## total_ac         9.373e-08  1.645e-07   0.570    0.571    
## group1:total_ac -3.910e-07  3.366e-07  -1.161    0.249    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06844 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.03198,    Adjusted R-squared:  -0.008926 
## F-statistic: 0.7818 on 3 and 71 DF,  p-value: 0.508
summary(lm(dmn_modularity_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = dmn_modularity_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.11475 -0.03987 -0.00162  0.03485  0.14747 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.267e-01  1.990e-02  16.419   <2e-16 ***
## group1      -2.418e-02  1.330e-02  -1.818   0.0732 .  
## total_ac     1.589e-07  1.182e-07   1.345   0.1830    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05635 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.08061,    Adjusted R-squared:  0.05507 
## F-statistic: 3.157 on 2 and 72 DF,  p-value: 0.04852
summary(lm(dmn_efficiency_x ~ group + total_ac, data = d)) #
## 
## Call:
## lm(formula = dmn_efficiency_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.71151 -0.22305 -0.00039  0.15870  1.02764 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.096e+00  1.202e-01  25.768  < 2e-16 ***
## group1      -3.701e-01  8.032e-02  -4.608 1.72e-05 ***
## total_ac     1.193e-06  7.137e-07   1.672   0.0989 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3403 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2809, Adjusted R-squared:  0.2609 
## F-statistic: 14.06 on 2 and 72 DF,  p-value: 7e-06
summary(lm(dmn_clustering_x ~ group + total_ac, data = d)) # *
## 
## Call:
## lm(formula = dmn_clustering_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.06026 -0.01865 -0.00243  0.01630  0.08172 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.762e-01  1.006e-02  27.450   <2e-16 ***
## group1      -3.217e-03  6.726e-03  -0.478    0.634    
## total_ac     1.256e-07  5.976e-08   2.102    0.039 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02849 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06789,    Adjusted R-squared:  0.042 
## F-statistic: 2.622 on 2 and 72 DF,  p-value: 0.07959
summary(lm(dmn_participation_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = dmn_participation_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.19015 -0.03766  0.00310  0.04979  0.13816 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.822e-01  2.423e-02  19.904   <2e-16 ***
## group1      -1.580e-02  1.619e-02  -0.976    0.332    
## total_ac     3.322e-10  1.439e-07   0.002    0.998    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06861 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.01358,    Adjusted R-squared:  -0.01382 
## F-statistic: 0.4958 on 2 and 72 DF,  p-value: 0.6112
summary(lm(fpn_modularity_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.141839 -0.042182  0.005995  0.048093  0.114177 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.38298    0.04118   9.301 7.44e-14 ***
## group1       0.01343    0.05598   0.240    0.811    
## IS           0.02188    0.10022   0.218    0.828    
## group1:IS   -0.05903    0.12640  -0.467    0.642    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06466 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01404,    Adjusted R-squared:  -0.02822 
## F-statistic: 0.3322 on 3 and 70 DF,  p-value: 0.8021
summary(lm(fpn_efficiency_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * IS, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.82652 -0.21188 -0.02649  0.19262  1.08523 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.74193    0.22679  12.090   <2e-16 ***
## group1      -0.05895    0.30834  -0.191    0.849    
## IS           0.43196    0.55199   0.783    0.437    
## group1:IS   -0.24265    0.69618  -0.349    0.728    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3561 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05049,    Adjusted R-squared:  0.009798 
## F-statistic: 1.241 on 3 and 70 DF,  p-value: 0.3015
summary(lm(fpn_clustering_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.073104 -0.021323  0.005808  0.019413  0.075319 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.31432    0.02074  15.156   <2e-16 ***
## group1      -0.01863    0.02820  -0.661    0.511    
## IS          -0.06880    0.05048  -1.363    0.177    
## group1:IS    0.06562    0.06366   1.031    0.306    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03257 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.03817,    Adjusted R-squared:  -0.003051 
## F-statistic: 0.926 on 3 and 70 DF,  p-value: 0.4329
summary(lm(fpn_participation_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group * IS, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25782 -0.04083  0.02018  0.03950  0.13087 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.63874    0.04615  13.842   <2e-16 ***
## group1      -0.03754    0.06274  -0.598    0.551    
## IS          -0.11066    0.11231  -0.985    0.328    
## group1:IS    0.16869    0.14165   1.191    0.238    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07246 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.07196,    Adjusted R-squared:  0.03218 
## F-statistic: 1.809 on 3 and 70 DF,  p-value: 0.1534
summary(lm(fpn_modularity_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + IS, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14204 -0.03888  0.00439  0.04814  0.11060 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.39774    0.02622  15.168   <2e-16 ***
## group1      -0.01166    0.01564  -0.745    0.459    
## IS          -0.01523    0.06073  -0.251    0.803    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0643 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01097,    Adjusted R-squared:  -0.01689 
## F-statistic: 0.3936 on 2 and 71 DF,  p-value: 0.6761
summary(lm(fpn_efficiency_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + IS, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.83384 -0.21432 -0.01508  0.18096  1.10079 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.80264    0.14433  19.418   <2e-16 ***
## group1      -0.16209    0.08608  -1.883   0.0638 .  
## IS           0.27942    0.33428   0.836   0.4060    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3539 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.04884,    Adjusted R-squared:  0.02205 
## F-statistic: 1.823 on 2 and 71 DF,  p-value: 0.169
summary(lm(fpn_clustering_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group + IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.073613 -0.021925  0.006085  0.020493  0.075541 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.297896   0.013287  22.420   <2e-16 ***
## group1       0.009268   0.007924   1.170    0.246    
## IS          -0.027546   0.030773  -0.895    0.374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03258 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.02357,    Adjusted R-squared:  -0.003935 
## F-statistic: 0.857 on 2 and 71 DF,  p-value: 0.4288
summary(lm(fpn_participation_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group + IS, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25273 -0.03729  0.02368  0.04047  0.12111 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.596529   0.029638  20.127   <2e-16 ***
## group1       0.034162   0.017675   1.933   0.0573 .  
## IS          -0.004614   0.068643  -0.067   0.9466    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07267 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05315,    Adjusted R-squared:  0.02648 
## F-statistic: 1.993 on 2 and 71 DF,  p-value: 0.1438
#---------------------------------------------------------

summary(lm(fpn_modularity_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.156812 -0.043619  0.009282  0.046264  0.108921 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.48198    0.03819  12.622   <2e-16 ***
## group1      -0.08616    0.05336  -1.615   0.1109    
## IV          -0.09863    0.04031  -2.447   0.0169 *  
## group1:IV    0.07898    0.05787   1.365   0.1767    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06209 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.09077,    Adjusted R-squared:  0.0518 
## F-statistic: 2.329 on 3 and 70 DF,  p-value: 0.08182
summary(lm(fpn_efficiency_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * IV, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8151 -0.1835 -0.0117  0.1843  1.1107 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.3941     0.2082  16.304   <2e-16 ***
## group1       -0.3029     0.2909  -1.041   0.3014    
## IV           -0.5246     0.2197  -2.387   0.0197 *  
## group1:IV     0.1537     0.3155   0.487   0.6277    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3385 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1422, Adjusted R-squared:  0.1055 
## F-statistic: 3.869 on 3 and 70 DF,  p-value: 0.01278
summary(lm(fpn_clustering_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.073046 -0.019868  0.004755  0.018798  0.074764 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29982    0.02003  14.969   <2e-16 ***
## group1       0.01667    0.02799   0.596    0.553    
## IV          -0.01408    0.02114  -0.666    0.508    
## group1:IV   -0.01184    0.03035  -0.390    0.698    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03257 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.0381, Adjusted R-squared:  -0.003124 
## F-statistic: 0.9242 on 3 and 70 DF,  p-value: 0.4337
summary(lm(fpn_participation_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group * IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25322 -0.03977  0.02444  0.04070  0.12295 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.54923    0.04466  12.299   <2e-16 ***
## group1       0.07364    0.06240   1.180    0.242    
## IV           0.04965    0.04714   1.053    0.296    
## group1:IV   -0.04309    0.06767  -0.637    0.526    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07261 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.06811,    Adjusted R-squared:  0.02817 
## F-statistic: 1.705 on 3 and 70 DF,  p-value: 0.1738
summary(lm(fpn_modularity_x ~ group + IV, data = d)) # *
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.16210 -0.03644  0.01300  0.04502  0.09477 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.44690    0.02841  15.730   <2e-16 ***
## group1      -0.01610    0.01466  -1.098   0.2759    
## IV          -0.06031    0.02910  -2.073   0.0418 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06247 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.06657,    Adjusted R-squared:  0.04028 
## F-statistic: 2.532 on 2 and 71 DF,  p-value: 0.08667
summary(lm(fpn_efficiency_x ~ group + IV, data = d)) # **
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84122 -0.19704 -0.01341  0.17411  1.11331 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.32583    0.15312  21.721  < 2e-16 ***
## group1      -0.16657    0.07901  -2.108  0.03853 *  
## IV          -0.45001    0.15682  -2.870  0.00541 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3367 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1393, Adjusted R-squared:  0.1151 
## F-statistic: 5.746 on 2 and 71 DF,  p-value: 0.004865
summary(lm(fpn_clustering_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group + IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.073044 -0.017420  0.003783  0.019505  0.074887 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.305078   0.014723  20.721   <2e-16 ***
## group1       0.006166   0.007597   0.812    0.420    
## IV          -0.019820   0.015079  -1.314    0.193    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03237 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.03601,    Adjusted R-squared:  0.008854 
## F-statistic: 1.326 on 2 and 71 DF,  p-value: 0.272
summary(lm(fpn_participation_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group + IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25292 -0.03980  0.02399  0.04195  0.12200 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.56837    0.03289  17.283   <2e-16 ***
## group1       0.03541    0.01697   2.087   0.0405 *  
## IV           0.02875    0.03368   0.854   0.3962    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07231 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.06271,    Adjusted R-squared:  0.03631 
## F-statistic: 2.375 on 2 and 71 DF,  p-value: 0.1003
#---------------------------------------------------------

summary(lm(fpn_modularity_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.141862 -0.033786  0.005158  0.044684  0.126704 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.46231    0.05764   8.021 1.67e-11 ***
## group1      -0.11933    0.08596  -1.388    0.169    
## RA          -0.08410    0.06757  -1.245    0.217    
## group1:RA    0.12702    0.10097   1.258    0.213    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06393 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.03593,    Adjusted R-squared:  -0.005384 
## F-statistic: 0.8697 on 3 and 70 DF,  p-value: 0.461
summary(lm(fpn_efficiency_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.85033 -0.18945 -0.01325  0.13893  1.13963 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.0005     0.3160   9.496 3.28e-14 ***
## group1        0.3707     0.4713   0.787    0.434    
## RA           -0.1032     0.3704  -0.279    0.781    
## group1:RA    -0.6122     0.5536  -1.106    0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3505 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.08025,    Adjusted R-squared:  0.04083 
## F-statistic: 2.036 on 3 and 70 DF,  p-value: 0.1167
summary(lm(fpn_clustering_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.071967 -0.019264  0.001985  0.021371  0.073050 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.36363    0.02792  13.026  < 2e-16 ***
## group1      -0.02984    0.04164  -0.717  0.47599    
## RA          -0.09133    0.03273  -2.791  0.00677 ** 
## group1:RA    0.04404    0.04891   0.901  0.37092    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03097 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1303, Adjusted R-squared:  0.09308 
## F-statistic: 3.497 on 3 and 70 DF,  p-value: 0.01993
summary(lm(fpn_participation_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group * RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.24662 -0.03858  0.01895  0.04502  0.11589 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.56374    0.06495   8.680 1.02e-12 ***
## group1      -0.03573    0.09687  -0.369    0.713    
## RA           0.03686    0.07614   0.484    0.630    
## group1:RA    0.08317    0.11378   0.731    0.467    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07205 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.08258,    Adjusted R-squared:  0.04326 
## F-statistic:   2.1 on 3 and 70 DF,  p-value: 0.108
summary(lm(fpn_modularity_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.142377 -0.038324  0.006292  0.047144  0.112450 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.41454    0.04354   9.522 2.57e-14 ***
## group1      -0.01283    0.01498  -0.857    0.394    
## RA          -0.02722    0.05042  -0.540    0.591    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0642 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01414,    Adjusted R-squared:  -0.01363 
## F-statistic: 0.5091 on 2 and 71 DF,  p-value: 0.6032
summary(lm(fpn_efficiency_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.85848 -0.18106 -0.02359  0.16609  1.16712 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.23073    0.23808  13.570   <2e-16 ***
## group1      -0.14263    0.08189  -1.742   0.0859 .  
## RA          -0.37735    0.27570  -1.369   0.1754    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3511 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.06418,    Adjusted R-squared:  0.03781 
## F-statistic: 2.434 on 2 and 71 DF,  p-value: 0.09493
summary(lm(fpn_clustering_x ~ group + RA, data = d)) # **
## 
## Call:
## lm(formula = fpn_clustering_x ~ group + RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.071416 -0.020194  0.004119  0.021199  0.071899 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.347068   0.020973  16.548  < 2e-16 ***
## group1       0.007089   0.007214   0.983  0.32912    
## RA          -0.071611   0.024287  -2.949  0.00432 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03093 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1203, Adjusted R-squared:  0.09549 
## F-statistic: 4.853 on 2 and 71 DF,  p-value: 0.01058
summary(lm(fpn_participation_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group + RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.24067 -0.04059  0.02030  0.04786  0.11104 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.53246    0.04870  10.934   <2e-16 ***
## group1       0.03400    0.01675   2.030   0.0461 *  
## RA           0.07410    0.05639   1.314   0.1931    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07181 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.07557,    Adjusted R-squared:  0.04953 
## F-statistic: 2.902 on 2 and 71 DF,  p-value: 0.06144
#---------------------------------------------------------
summary(lm(fpn_modularity_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.140671 -0.042290  0.004583  0.048751  0.110322 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.912e-01  2.541e-02  15.399   <2e-16 ***
## group1      -2.807e-02  3.643e-02  -0.770    0.444    
## fact         1.129e-07  5.709e-06   0.020    0.984    
## group1:fact  3.884e-06  7.816e-06   0.497    0.621    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06455 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.01534,    Adjusted R-squared:  -0.02748 
## F-statistic: 0.3582 on 3 and 69 DF,  p-value: 0.7833
summary(lm(fpn_efficiency_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.80302 -0.20754 -0.02366  0.18303  1.06211 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.786e+00  1.336e-01  20.846   <2e-16 ***
## group1      -1.879e-01  1.917e-01  -0.980    0.330    
## fact         3.136e-05  3.003e-05   1.044    0.300    
## group1:fact  1.330e-05  4.111e-05   0.324    0.747    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3395 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.07778,    Adjusted R-squared:  0.03769 
## F-statistic:  1.94 on 3 and 69 DF,  p-value: 0.1312
summary(lm(fpn_clustering_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.075338 -0.022109  0.003819  0.020138  0.072927 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.987e-01  1.269e-02  23.528   <2e-16 ***
## group1      -4.521e-04  1.821e-02  -0.025    0.980    
## fact        -2.883e-06  2.852e-06  -1.011    0.316    
## group1:fact  2.360e-06  3.905e-06   0.604    0.548    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03225 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.03448,    Adjusted R-squared:  -0.007497 
## F-statistic: 0.8214 on 3 and 69 DF,  p-value: 0.4865
summary(lm(fpn_participation_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group * fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25744 -0.03448  0.02448  0.04028  0.13706 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  6.124e-01  2.884e-02  21.233   <2e-16 ***
## group1       1.909e-03  4.136e-02   0.046    0.963    
## fact        -4.349e-06  6.481e-06  -0.671    0.504    
## group1:fact  7.374e-06  8.873e-06   0.831    0.409    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07328 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.0595, Adjusted R-squared:  0.01861 
## F-statistic: 1.455 on 3 and 69 DF,  p-value: 0.2344
summary(lm(fpn_modularity_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.142210 -0.046122  0.006569  0.048626  0.111343 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.828e-01  1.878e-02  20.377   <2e-16 ***
## group1      -1.162e-02  1.515e-02  -0.767    0.446    
## fact         2.185e-06  3.878e-06   0.563    0.575    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0642 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.01181,    Adjusted R-squared:  -0.01642 
## F-statistic: 0.4183 on 2 and 70 DF,  p-value: 0.6598
summary(lm(fpn_efficiency_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.79300 -0.21844 -0.02739  0.18397  1.04691 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.757e+00  9.871e-02  27.931   <2e-16 ***
## group1      -1.315e-01  7.964e-02  -1.651   0.1031    
## fact         3.845e-05  2.038e-05   1.887   0.0633 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3374 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.07638,    Adjusted R-squared:  0.04999 
## F-statistic: 2.895 on 2 and 70 DF,  p-value: 0.06197
summary(lm(fpn_clustering_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group + fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.076545 -0.023179  0.005353  0.020567  0.071588 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.936e-01  9.394e-03  31.249   <2e-16 ***
## group1       9.539e-03  7.579e-03   1.259    0.212    
## fact        -1.624e-06  1.939e-06  -0.838    0.405    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0321 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.02937,    Adjusted R-squared:  0.001642 
## F-statistic: 1.059 on 2 and 70 DF,  p-value: 0.3522
summary(lm(fpn_participation_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group + fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25298 -0.03741  0.02388  0.04105  0.12225 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.964e-01  2.139e-02  27.877   <2e-16 ***
## group1       3.313e-02  1.726e-02   1.920    0.059 .  
## fact        -4.149e-07  4.417e-06  -0.094    0.925    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07311 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.05009,    Adjusted R-squared:  0.02295 
## F-statistic: 1.845 on 2 and 70 DF,  p-value: 0.1656
#---------------------------------------------------------

summary(lm(fpn_modularity_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.143840 -0.039318  0.005595  0.043191  0.109942 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.378005   0.069506   5.438 7.66e-07 ***
## group1        -0.002629   0.093094  -0.028    0.978    
## actamp         0.008749   0.043967   0.199    0.843    
## group1:actamp -0.005157   0.059629  -0.086    0.931    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06479 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.008013,   Adjusted R-squared:  -0.03512 
## F-statistic: 0.1858 on 3 and 69 DF,  p-value: 0.9057
summary(lm(fpn_efficiency_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84272 -0.19379 -0.01241  0.18439  1.07880 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    2.67933    0.37058   7.230 5.08e-10 ***
## group1        -0.16719    0.49634  -0.337    0.737    
## actamp         0.14999    0.23442   0.640    0.524    
## group1:actamp  0.03739    0.31792   0.118    0.907    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3454 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.0456, Adjusted R-squared:  0.004102 
## F-statistic: 1.099 on 3 and 69 DF,  p-value: 0.3556
summary(lm(fpn_clustering_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.071672 -0.023188  0.007212  0.019586  0.074209 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.3075678  0.0346799   8.869 5.17e-13 ***
## group1         0.0070893  0.0464489   0.153    0.879    
## actamp        -0.0131972  0.0219375  -0.602    0.549    
## group1:actamp  0.0008028  0.0297519   0.027    0.979    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03232 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.03008,    Adjusted R-squared:  -0.01209 
## F-statistic: 0.7133 on 3 and 69 DF,  p-value: 0.5474
summary(lm(fpn_participation_x ~ group * actamp, data = d)) # **
## 
## Call:
## lm(formula = fpn_participation_x ~ group * actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.21472 -0.03441  0.01477  0.04464  0.14166 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.79657    0.07461  10.676 2.94e-16 ***
## group1        -0.23068    0.09993  -2.308  0.02398 *  
## actamp        -0.12911    0.04720  -2.735  0.00791 ** 
## group1:actamp  0.16999    0.06401   2.656  0.00982 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06955 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1528, Adjusted R-squared:  0.116 
## F-statistic: 4.149 on 3 and 69 DF,  p-value: 0.009204
summary(lm(fpn_modularity_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.143693 -0.038863  0.005236  0.044023  0.110046 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.382388   0.047218   8.098  1.2e-11 ***
## group1      -0.010570   0.015204  -0.695    0.489    
## actamp       0.005945   0.029489   0.202    0.841    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06432 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.007905,   Adjusted R-squared:  -0.02044 
## F-statistic: 0.2789 on 2 and 70 DF,  p-value: 0.7575
summary(lm(fpn_efficiency_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84211 -0.19284 -0.01694  0.18221  1.07195 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.64754    0.25176  10.516 4.73e-16 ***
## group1      -0.10961    0.08107  -1.352    0.181    
## actamp       0.17032    0.15723   1.083    0.282    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.343 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.04541,    Adjusted R-squared:  0.01813 
## F-statistic: 1.665 on 2 and 70 DF,  p-value: 0.1966
summary(lm(fpn_clustering_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group + actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.071581 -0.023257  0.007259  0.019539  0.074228 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.306885   0.023558  13.027   <2e-16 ***
## group1       0.008326   0.007586   1.098    0.276    
## actamp      -0.012761   0.014713  -0.867    0.389    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03209 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.03007,    Adjusted R-squared:  0.002357 
## F-statistic: 1.085 on 2 and 70 DF,  p-value: 0.3435
#---------------------------------------------------------

summary(lm(fpn_modularity_x ~ group * actphi, data = d)) # *
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.149322 -0.048833  0.004452  0.042657  0.104679 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.096351   0.110819   0.869  0.38762   
## group1         0.361334   0.154279   2.342  0.02207 * 
## actphi         0.017999   0.006728   2.675  0.00932 **
## group1:actphi -0.023163   0.009835  -2.355  0.02137 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06148 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1067, Adjusted R-squared:  0.06786 
## F-statistic: 2.747 on 3 and 69 DF,  p-value: 0.04942
summary(lm(fpn_efficiency_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * actphi, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.82828 -0.21613 -0.02681  0.17525  1.12499 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    3.039576   0.627522   4.844 7.53e-06 ***
## group1        -0.362602   0.873620  -0.415    0.679    
## actphi        -0.007662   0.038096  -0.201    0.841    
## group1:actphi  0.015618   0.055693   0.280    0.780    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3481 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.03051,    Adjusted R-squared:  -0.01164 
## F-statistic: 0.7239 on 3 and 69 DF,  p-value: 0.5412
summary(lm(fpn_clustering_x ~ group * actphi, data = d)) # *
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.067691 -0.022372  0.003912  0.018512  0.071636 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.236492   0.056365   4.196 7.96e-05 ***
## group1         0.177466   0.078470   2.262   0.0269 *  
## actphi         0.003074   0.003422   0.898   0.3721    
## group1:actphi -0.011003   0.005002  -2.200   0.0312 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03127 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.09237,    Adjusted R-squared:  0.05291 
## F-statistic: 2.341 on 3 and 69 DF,  p-value: 0.08083
summary(lm(fpn_participation_x ~ group * actphi, data = d)) # *
## 
## Call:
## lm(formula = fpn_participation_x ~ group * actphi, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23260 -0.04180  0.01686  0.04158  0.10572 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.822170   0.127057   6.471 1.19e-08 ***
## group1        -0.409835   0.176885  -2.317   0.0235 *  
## actphi        -0.013863   0.007713  -1.797   0.0767 .  
## group1:actphi  0.028329   0.011276   2.512   0.0143 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07049 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1297, Adjusted R-squared:  0.09188 
## F-statistic: 3.428 on 3 and 69 DF,  p-value: 0.02174
summary(lm(fpn_efficiency_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + actphi, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84638 -0.22266 -0.03174  0.17873  1.12909 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.9196647  0.4562583   6.399 1.53e-08 ***
## group1      -0.1189754  0.0915378  -1.300    0.198    
## actphi      -0.0003545  0.0276055  -0.013    0.990    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3458 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.02941,    Adjusted R-squared:  0.001677 
## F-statistic:  1.06 on 2 and 70 DF,  p-value: 0.3518
#---------------------------------------------------------
summary(lm(fpn_modularity_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.139845 -0.042491  0.006563  0.044560  0.118075 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.4257956  0.0688763   6.182 3.57e-08 ***
## group1            -0.1227495  0.0924539  -1.328    0.189    
## sleep_time        -0.0001038  0.0002074  -0.501    0.618    
## group1:sleep_time  0.0003207  0.0002693   1.191    0.238    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06359 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.03409,    Adjusted R-squared:  -0.006723 
## F-statistic: 0.8353 on 3 and 71 DF,  p-value: 0.479
summary(lm(fpn_efficiency_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.83060 -0.19883 -0.01336  0.23905  1.04405 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        3.400808   0.376697   9.028 2.08e-13 ***
## group1            -0.181201   0.505648  -0.358    0.721    
## sleep_time        -0.001482   0.001134  -1.307    0.196    
## group1:sleep_time  0.000200   0.001473   0.136    0.892    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3478 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.08885,    Adjusted R-squared:  0.05035 
## F-statistic: 2.308 on 3 and 71 DF,  p-value: 0.08385
summary(lm(fpn_clustering_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.071708 -0.021840  0.005037  0.021193  0.075258 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.3340084  0.0350393   9.532 2.46e-14 ***
## group1            -0.0470221  0.0470339  -1.000    0.321    
## sleep_time        -0.0001433  0.0001055  -1.358    0.179    
## group1:sleep_time  0.0001630  0.0001370   1.190    0.238    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03235 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0376, Adjusted R-squared:  -0.00306 
## F-statistic: 0.9248 on 3 and 71 DF,  p-value: 0.4334
summary(lm(fpn_participation_x ~ group * sleep_time, data = d)) # 
## 
## Call:
## lm(formula = fpn_participation_x ~ group * sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.20699 -0.03674  0.02192  0.04655  0.11940 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.3824571  0.0745695   5.129 2.43e-06 ***
## group1             0.2458485  0.1000960   2.456   0.0165 *  
## sleep_time         0.0006459  0.0002245   2.877   0.0053 ** 
## group1:sleep_time -0.0006431  0.0002916  -2.206   0.0306 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06885 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1547, Adjusted R-squared:  0.119 
## F-statistic: 4.332 on 3 and 71 DF,  p-value: 0.007328
summary(lm(fpn_modularity_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.141727 -0.043545  0.006059  0.049884  0.110718 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.633e-01  4.475e-02   8.119 9.18e-12 ***
## group1      -1.412e-02  1.513e-02  -0.934    0.354    
## sleep_time   8.636e-05  1.327e-04   0.651    0.517    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06377 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0148, Adjusted R-squared:  -0.01257 
## F-statistic: 0.5407 on 2 and 72 DF,  p-value: 0.5847
summary(lm(fpn_efficiency_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.83193 -0.19791 -0.01558  0.23279  1.05087 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.3618309  0.2423508  13.872   <2e-16 ***
## group1      -0.1134442  0.0819274  -1.385   0.1704    
## sleep_time  -0.0013634  0.0007186  -1.897   0.0618 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3454 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.08861,    Adjusted R-squared:  0.0633 
## F-statistic:   3.5 on 2 and 72 DF,  p-value: 0.03543
summary(lm(fpn_clustering_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group + sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.075377 -0.023381  0.005328  0.019689  0.076173 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.023e-01  2.276e-02  13.278   <2e-16 ***
## group1       8.182e-03  7.695e-03   1.063    0.291    
## sleep_time  -4.663e-05  6.750e-05  -0.691    0.492    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03244 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.01842,    Adjusted R-squared:  -0.008845 
## F-statistic: 0.6756 on 2 and 72 DF,  p-value: 0.512
#---------------------------------------------------------
summary(lm(fpn_modularity_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14106 -0.03892  0.00468  0.04901  0.11089 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.4048181  0.1187170   3.410  0.00108 **
## group1            -0.0953104  0.1628305  -0.585  0.56018   
## efficiency        -0.0001831  0.0016492  -0.111  0.91190   
## group1:efficiency  0.0011558  0.0022542   0.513  0.60973   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06422 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.01473,    Adjusted R-squared:  -0.0269 
## F-statistic: 0.3538 on 3 and 71 DF,  p-value: 0.7865
summary(lm(fpn_efficiency_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84956 -0.17832 -0.01858  0.16903  1.11735 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        2.729961   0.654660   4.170 8.49e-05 ***
## group1             0.593322   0.897922   0.661    0.511    
## efficiency         0.002564   0.009095   0.282    0.779    
## group1:efficiency -0.010279   0.012431  -0.827    0.411    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3542 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.05514,    Adjusted R-squared:  0.01521 
## F-statistic: 1.381 on 3 and 71 DF,  p-value: 0.2556
summary(lm(fpn_clustering_x ~ group * efficiency, data = d)) # 
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.072255 -0.019434  0.003844  0.022802  0.074609 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.4546363  0.0571713   7.952 2.05e-11 ***
## group1            -0.1758022  0.0784153  -2.242  0.02809 *  
## efficiency        -0.0023383  0.0007942  -2.944  0.00437 ** 
## group1:efficiency  0.0025478  0.0010856   2.347  0.02172 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03093 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1203, Adjusted R-squared:  0.08314 
## F-statistic: 3.237 on 3 and 71 DF,  p-value: 0.02719
summary(lm(fpn_participation_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group * efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25452 -0.03968  0.02329  0.04250  0.10905 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.493655   0.133927   3.686 0.000442 ***
## group1             0.124353   0.183692   0.677 0.500629    
## efficiency         0.001409   0.001861   0.757 0.451437    
## group1:efficiency -0.001252   0.002543  -0.492 0.623926    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07245 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06384,    Adjusted R-squared:  0.02428 
## F-statistic: 1.614 on 3 and 71 DF,  p-value: 0.1938
summary(lm(fpn_modularity_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.142118 -0.042378  0.006454  0.047159  0.109121 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.3604468  0.0808528   4.458 2.98e-05 ***
## group1      -0.0121705  0.0147975  -0.822    0.414    
## efficiency   0.0004355  0.0011185   0.389    0.698    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06389 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.01108,    Adjusted R-squared:  -0.01639 
## F-statistic: 0.4034 on 2 and 72 DF,  p-value: 0.6695
summary(lm(fpn_efficiency_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84461 -0.19852 -0.01795  0.18429  1.14297 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.124576   0.447174   6.987 1.17e-09 ***
## group1      -0.146079   0.081841  -1.785   0.0785 .  
## efficiency  -0.002938   0.006186  -0.475   0.6362    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3534 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.04604,    Adjusted R-squared:  0.01954 
## F-statistic: 1.737 on 2 and 72 DF,  p-value: 0.1833
summary(lm(fpn_participation_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group + efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25356 -0.03813  0.02255  0.04318  0.11459 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.5417301  0.0911987   5.940 9.26e-08 ***
## group1      0.0342733  0.0166910   2.053   0.0437 *  
## efficiency  0.0007385  0.0012616   0.585   0.5602    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07207 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06064,    Adjusted R-squared:  0.03455 
## F-statistic: 2.324 on 2 and 72 DF,  p-value: 0.1052
#---------------------------------------------------------
summary(lm(fpn_modularity_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group * total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.142650 -0.042525  0.008982  0.050319  0.111095 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      4.004e-01  2.536e-02  15.789   <2e-16 ***
## group1          -1.838e-03  4.489e-02  -0.041    0.967    
## total_ac        -5.803e-08  1.544e-07  -0.376    0.708    
## group1:total_ac -8.882e-08  3.159e-07  -0.281    0.779    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06422 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0149, Adjusted R-squared:  -0.02672 
## F-statistic: 0.358 on 3 and 71 DF,  p-value: 0.7835
summary(lm(fpn_efficiency_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group * total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.83603 -0.20011 -0.01572  0.21757  1.12379 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      2.867e+00  1.372e-01  20.907   <2e-16 ***
## group1          -4.665e-01  2.428e-01  -1.922   0.0586 .  
## total_ac         3.080e-07  8.349e-07   0.369   0.7133    
## group1:total_ac  2.536e-06  1.708e-06   1.485   0.1420    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3473 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0914, Adjusted R-squared:  0.05301 
## F-statistic: 2.381 on 3 and 71 DF,  p-value: 0.07674
summary(lm(fpn_clustering_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group * total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.072743 -0.021272  0.005475  0.020875  0.074510 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      2.723e-01  1.280e-02  21.271   <2e-16 ***
## group1           2.562e-02  2.266e-02   1.131    0.262    
## total_ac         9.729e-08  7.792e-08   1.249    0.216    
## group1:total_ac -1.281e-07  1.594e-07  -0.803    0.424    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03241 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0338, Adjusted R-squared:  -0.007027 
## F-statistic: 0.8279 on 3 and 71 DF,  p-value: 0.4829
summary(lm(fpn_participation_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group * total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.24756 -0.03785  0.02232  0.04113  0.11815 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      6.178e-01  2.857e-02  21.623   <2e-16 ***
## group1           5.337e-03  5.057e-02   0.106    0.916    
## total_ac        -1.532e-07  1.739e-07  -0.881    0.381    
## group1:total_ac  2.015e-07  3.558e-07   0.566    0.573    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07234 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06669,    Adjusted R-squared:  0.02726 
## F-statistic: 1.691 on 3 and 71 DF,  p-value: 0.1767
summary(lm(fpn_modularity_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = fpn_modularity_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14280 -0.04105  0.00705  0.04948  0.10868 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.036e-01  2.253e-02  17.914   <2e-16 ***
## group1      -1.372e-02  1.506e-02  -0.911    0.365    
## total_ac    -7.924e-08  1.338e-07  -0.592    0.556    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06381 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0138, Adjusted R-squared:  -0.01359 
## F-statistic: 0.5039 on 2 and 72 DF,  p-value: 0.6063
summary(lm(fpn_efficiency_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = fpn_efficiency_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.81396 -0.21337 -0.02035  0.21833  1.11298 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.776e+00  1.237e-01  22.452   <2e-16 ***
## group1      -1.273e-01  8.265e-02  -1.540    0.128    
## total_ac     9.138e-07  7.344e-07   1.244    0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3502 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06319,    Adjusted R-squared:  0.03717 
## F-statistic: 2.428 on 2 and 72 DF,  p-value: 0.09538
summary(lm(fpn_clustering_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = fpn_clustering_x ~ group + total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.072916 -0.023746  0.006168  0.020668  0.077739 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.769e-01  1.142e-02  24.252   <2e-16 ***
## group1      8.491e-03  7.632e-03   1.113    0.270    
## total_ac    6.669e-08  6.781e-08   0.984    0.329    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03233 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.02502,    Adjusted R-squared:  -0.002068 
## F-statistic: 0.9237 on 2 and 72 DF,  p-value: 0.4017
summary(lm(fpn_participation_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = fpn_participation_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.24912 -0.03742  0.02277  0.04299  0.11895 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  6.105e-01  2.542e-02  24.014   <2e-16 ***
## group1       3.230e-02  1.699e-02   1.900   0.0614 .  
## total_ac    -1.051e-07  1.510e-07  -0.696   0.4888    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.072 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06247,    Adjusted R-squared:  0.03643 
## F-statistic: 2.399 on 2 and 72 DF,  p-value: 0.09804
summary(lm(wb_modularity_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.208184 -0.043571  0.009253  0.056177  0.127095 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.43298    0.04731   9.152 1.39e-13 ***
## group1      -0.02496    0.06432  -0.388    0.699    
## IS          -0.05450    0.11515  -0.473    0.637    
## group1:IS    0.07055    0.14523   0.486    0.629    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07429 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.004524,   Adjusted R-squared:  -0.03814 
## F-statistic: 0.106 on 3 and 70 DF,  p-value: 0.9563
summary(lm(wb_efficiency_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * IS, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0240 -0.2466 -0.1117  0.2580  1.4253 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2.9841     0.2806  10.635  2.9e-16 ***
## group1       -0.2333     0.3815  -0.612    0.543    
## IS            0.5568     0.6829   0.815    0.418    
## group1:IS    -0.2493     0.8613  -0.289    0.773    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4406 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1262, Adjusted R-squared:  0.0888 
## F-statistic: 3.371 on 3 and 70 DF,  p-value: 0.02319
summary(lm(wb_clustering_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.103275 -0.026542  0.002192  0.028193  0.059429 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21140    0.02242   9.430 4.32e-14 ***
## group1       0.02229    0.03048   0.731    0.467    
## IS           0.04078    0.05456   0.747    0.457    
## group1:IS   -0.04833    0.06881  -0.702    0.485    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0352 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.009681,   Adjusted R-squared:  -0.03276 
## F-statistic: 0.2281 on 3 and 70 DF,  p-value: 0.8765
summary(lm(wb_participation_x ~ group * IS, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.090206 -0.022556  0.000652  0.021011  0.087680 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.55518    0.02213  25.092   <2e-16 ***
## group1      -0.02280    0.03008  -0.758    0.451    
## IS          -0.03861    0.05385  -0.717    0.476    
## group1:IS    0.08692    0.06792   1.280    0.205    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03474 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.07221,    Adjusted R-squared:  0.03245 
## F-statistic: 1.816 on 3 and 70 DF,  p-value: 0.1522
summary(lm(wb_modularity_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.213708 -0.042502  0.008059  0.054924  0.121074 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.415327   0.030134  13.783   <2e-16 ***
## group1       0.005027   0.017971   0.280    0.781    
## IS          -0.010148   0.069791  -0.145    0.885    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07389 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.001168,   Adjusted R-squared:  -0.02697 
## F-statistic: 0.04151 on 2 and 71 DF,  p-value: 0.9594
summary(lm(wb_efficiency_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + IS, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0315 -0.2455 -0.1144  0.2379  1.4413 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.0465     0.1785  17.065  < 2e-16 ***
## group1       -0.3393     0.1065  -3.187  0.00214 ** 
## IS            0.4000     0.4135   0.968  0.33656    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4377 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1252, Adjusted R-squared:  0.1006 
## F-statistic: 5.081 on 2 and 71 DF,  p-value: 0.008664
summary(lm(wb_clustering_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.104734 -0.025737  0.002164  0.027571  0.060521 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22349    0.01430  15.624   <2e-16 ***
## group1       0.00175    0.00853   0.205    0.838    
## IS           0.01040    0.03313   0.314    0.755    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03507 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.002704,   Adjusted R-squared:  -0.02539 
## F-statistic: 0.09624 on 2 and 71 DF,  p-value: 0.9084
summary(lm(wb_participation_x ~ group + IS, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + IS, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.087583 -0.022095  0.000098  0.021498  0.082652 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.533434   0.014233  37.479   <2e-16 ***
## group1      0.014148   0.008488   1.667   0.0999 .  
## IS          0.016033   0.032964   0.486   0.6282    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0349 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05051,    Adjusted R-squared:  0.02376 
## F-statistic: 1.888 on 2 and 71 DF,  p-value: 0.1589
#---------------------------------------------------------

summary(lm(wb_modularity_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.19311 -0.04350  0.01030  0.05611  0.12196 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.38635    0.04542   8.505 2.14e-12 ***
## group1       0.06618    0.06348   1.043    0.301    
## IV           0.02723    0.04795   0.568    0.572    
## group1:IV   -0.07020    0.06884  -1.020    0.311    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07386 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01604,    Adjusted R-squared:  -0.02613 
## F-statistic: 0.3804 on 3 and 70 DF,  p-value: 0.7674
summary(lm(wb_efficiency_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.85968 -0.26597 -0.07338  0.25043  1.46754 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.5822     0.2668  13.429   <2e-16 ***
## group1       -0.4211     0.3728  -1.129    0.263    
## IV           -0.4113     0.2816  -1.461    0.149    
## group1:IV     0.1025     0.4043   0.254    0.801    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4338 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1532, Adjusted R-squared:  0.1169 
## F-statistic: 4.221 on 3 and 70 DF,  p-value: 0.008406
summary(lm(wb_clustering_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.095980 -0.027021  0.000903  0.027929  0.058934 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.245864   0.021597  11.384   <2e-16 ***
## group1      -0.007546   0.030180  -0.250    0.803    
## IV          -0.019920   0.022797  -0.874    0.385    
## group1:IV    0.010408   0.032729   0.318    0.751    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03512 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01438,    Adjusted R-squared:  -0.02786 
## F-statistic: 0.3404 on 3 and 70 DF,  p-value: 0.7961
summary(lm(wb_participation_x ~ group * IV, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.088354 -0.022207  0.000874  0.025563  0.084129 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.398e-01  2.160e-02  24.994   <2e-16 ***
## group1      3.182e-03  3.018e-02   0.105    0.916    
## IV          4.267e-05  2.280e-02   0.002    0.999    
## group1:IV   1.411e-02  3.273e-02   0.431    0.668    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03512 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.05226,    Adjusted R-squared:  0.01164 
## F-statistic: 1.287 on 3 and 70 DF,  p-value: 0.2858
summary(lm(wb_modularity_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.208636 -0.041381  0.007075  0.053277  0.120649 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.417535   0.033602  12.426   <2e-16 ***
## group1       0.003911   0.017338   0.226    0.822    
## IV          -0.006824   0.034414  -0.198    0.843    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07388 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.001423,   Adjusted R-squared:  -0.02671 
## F-statistic: 0.0506 on 2 and 71 DF,  p-value: 0.9507
summary(lm(wb_efficiency_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + IV, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.88279 -0.28020 -0.06851  0.24184  1.46931 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.5367     0.1960  18.047  < 2e-16 ***
## group1       -0.3301     0.1011  -3.264  0.00169 ** 
## IV           -0.3615     0.2007  -1.801  0.07592 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4309 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1524, Adjusted R-squared:  0.1285 
## F-statistic: 6.383 on 2 and 71 DF,  p-value: 0.002823
summary(lm(wb_clustering_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.098325 -0.027222  0.000333  0.027886  0.058397 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.241241   0.015870  15.201   <2e-16 ***
## group1       0.001687   0.008189   0.206    0.837    
## IV          -0.014870   0.016253  -0.915    0.363    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03489 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.01296,    Adjusted R-squared:  -0.01485 
## F-statistic: 0.466 on 2 and 71 DF,  p-value: 0.6294
summary(lm(wb_participation_x ~ group + IV, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + IV, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.088453 -0.021964  0.001651  0.023980  0.084441 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.533510   0.015879  33.598   <2e-16 ***
## group1      0.015697   0.008193   1.916   0.0594 .  
## IV          0.006887   0.016263   0.423   0.6732    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03491 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.04974,    Adjusted R-squared:  0.02297 
## F-statistic: 1.858 on 2 and 71 DF,  p-value: 0.1634
#---------------------------------------------------------

summary(lm(wb_modularity_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.16770 -0.04748  0.01921  0.05028  0.11370 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.33433    0.06435   5.195 1.92e-06 ***
## group1      -0.06949    0.09598  -0.724    0.471    
## RA           0.09165    0.07544   1.215    0.229    
## group1:RA    0.08837    0.11274   0.784    0.436    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07139 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.08087,    Adjusted R-squared:  0.04148 
## F-statistic: 2.053 on 3 and 70 DF,  p-value: 0.1143
summary(lm(wb_efficiency_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.06869 -0.25336 -0.09223  0.21883  1.54274 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.71328    0.39030   9.514 3.04e-14 ***
## group1      -0.24850    0.58213  -0.427    0.671    
## RA          -0.60441    0.45758  -1.321    0.191    
## group1:RA   -0.07535    0.68378  -0.110    0.913    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.433 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1563, Adjusted R-squared:  0.1201 
## F-statistic: 4.322 on 3 and 70 DF,  p-value: 0.007459
summary(lm(wb_clustering_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.106156 -0.023331 -0.000377  0.026663  0.063877 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.25368    0.03130   8.104 1.18e-11 ***
## group1       0.02243    0.04669   0.480    0.632    
## RA          -0.03103    0.03670  -0.845    0.401    
## group1:RA   -0.02388    0.05484  -0.435    0.665    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03473 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.03615,    Adjusted R-squared:  -0.005156 
## F-statistic: 0.8752 on 3 and 70 DF,  p-value: 0.4582
summary(lm(wb_participation_x ~ group * RA, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.092803 -0.023001  0.000397  0.021547  0.087756 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.56321    0.03009  18.718   <2e-16 ***
## group1      -0.09643    0.04488  -2.149   0.0351 *  
## RA          -0.02786    0.03528  -0.790   0.4324    
## group1:RA    0.13338    0.05271   2.530   0.0137 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03338 on 70 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1437, Adjusted R-squared:  0.107 
## F-statistic: 3.917 on 3 and 70 DF,  p-value: 0.01206
summary(lm(wb_modularity_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.17206 -0.04517  0.01925  0.05078  0.11180 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.301095   0.048281   6.236 2.85e-08 ***
## group1      0.004602   0.016607   0.277   0.7825    
## RA          0.131222   0.055910   2.347   0.0217 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07119 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.07281,    Adjusted R-squared:  0.04669 
## F-statistic: 2.788 on 2 and 71 DF,  p-value: 0.06832
summary(lm(wb_efficiency_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + RA, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.06970 -0.25082 -0.09276  0.21782  1.54612 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.7416     0.2916  12.832  < 2e-16 ***
## group1       -0.3117     0.1003  -3.108  0.00271 ** 
## RA           -0.6382     0.3377  -1.890  0.06284 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4299 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.1561, Adjusted R-squared:  0.1324 
## F-statistic: 6.568 on 2 and 71 DF,  p-value: 0.002415
summary(lm(wb_clustering_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.106474 -0.023172 -0.000414  0.026037  0.062919 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.262661   0.023416  11.217   <2e-16 ***
## group1       0.002408   0.008054   0.299    0.766    
## RA          -0.041719   0.027116  -1.539    0.128    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03453 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.03354,    Adjusted R-squared:  0.006318 
## F-statistic: 1.232 on 2 and 71 DF,  p-value: 0.2978
summary(lm(wb_participation_x ~ group + RA, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + RA, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.083261 -0.021906 -0.000124  0.020854  0.079976 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.513051   0.023482  21.849   <2e-16 ***
## group1      0.015393   0.008077   1.906   0.0607 .  
## RA          0.031872   0.027192   1.172   0.2451    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03462 on 71 degrees of freedom
##   (60 observations deleted due to missingness)
## Multiple R-squared:  0.06542,    Adjusted R-squared:  0.0391 
## F-statistic: 2.485 on 2 and 71 DF,  p-value: 0.09053
#---------------------------------------------------------
summary(lm(wb_modularity_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.22026 -0.04373  0.01265  0.05371  0.13300 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.336e-01  2.927e-02  14.811   <2e-16 ***
## group1      -7.361e-03  4.198e-02  -0.175    0.861    
## fact        -5.463e-06  6.577e-06  -0.831    0.409    
## group1:fact  3.334e-06  9.005e-06   0.370    0.712    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07437 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.013,  Adjusted R-squared:  -0.02992 
## F-statistic: 0.3028 on 3 and 69 DF,  p-value: 0.8232
summary(lm(wb_efficiency_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.00847 -0.23542 -0.05151  0.22577  1.41801 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.084e+00  1.703e-01  18.109   <2e-16 ***
## group1      -4.652e-01  2.442e-01  -1.905   0.0609 .  
## fact         2.994e-05  3.826e-05   0.782   0.4366    
## group1:fact  3.529e-05  5.238e-05   0.674   0.5028    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4326 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1543, Adjusted R-squared:  0.1175 
## F-statistic: 4.195 on 3 and 69 DF,  p-value: 0.008716
summary(lm(wb_clustering_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10277 -0.02508  0.00199  0.02837  0.05837 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.205e-01  1.393e-02  15.829   <2e-16 ***
## group1       1.279e-02  1.998e-02   0.640    0.524    
## fact         1.744e-06  3.130e-06   0.557    0.579    
## group1:fact -2.298e-06  4.286e-06  -0.536    0.594    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03539 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.007162,   Adjusted R-squared:  -0.03601 
## F-statistic: 0.1659 on 3 and 69 DF,  p-value: 0.919
summary(lm(wb_participation_x ~ group * fact, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.088570 -0.022147 -0.000284  0.021492  0.084846 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.406e-01  1.347e-02  40.135   <2e-16 ***
## group1      -4.818e-03  1.932e-02  -0.249    0.804    
## fact        -1.908e-07  3.027e-06  -0.063    0.950    
## group1:fact  4.166e-06  4.144e-06   1.005    0.318    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03422 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.06499,    Adjusted R-squared:  0.02434 
## F-statistic: 1.599 on 3 and 69 DF,  p-value: 0.1976
summary(lm(wb_modularity_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.225718 -0.042275  0.008414  0.055371  0.127847 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.263e-01  2.163e-02  19.712   <2e-16 ***
## group1       6.756e-03  1.745e-02   0.387    0.700    
## fact        -3.684e-06  4.465e-06  -0.825    0.412    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07391 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.01104,    Adjusted R-squared:  -0.01722 
## F-statistic: 0.3905 on 2 and 70 DF,  p-value: 0.6782
summary(lm(wb_efficiency_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + fact, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.98192 -0.26288 -0.07033  0.21467  1.37768 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.007e+00  1.261e-01  23.848  < 2e-16 ***
## group1      -3.158e-01  1.017e-01  -3.104  0.00275 ** 
## fact         4.876e-05  2.603e-05   1.873  0.06520 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4309 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1487, Adjusted R-squared:  0.1244 
## F-statistic: 6.113 on 2 and 70 DF,  p-value: 0.003573
summary(lm(wb_clustering_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.104501 -0.025242  0.002023  0.028074  0.059896 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.255e-01  1.030e-02  21.888   <2e-16 ***
## group1      3.063e-03  8.312e-03   0.369    0.714    
## fact        5.185e-07  2.127e-06   0.244    0.808    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03521 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.003026,   Adjusted R-squared:  -0.02546 
## F-statistic: 0.1062 on 2 and 70 DF,  p-value: 0.8994
summary(lm(wb_participation_x ~ group + fact, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + fact, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.086045 -0.023090 -0.000936  0.019968  0.076481 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.315e-01  1.001e-02  53.081   <2e-16 ***
## group1      1.282e-02  8.079e-03   1.587    0.117    
## fact        2.031e-06  2.067e-06   0.983    0.329    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03422 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.05129,    Adjusted R-squared:  0.02419 
## F-statistic: 1.892 on 2 and 70 DF,  p-value: 0.1584
#---------------------------------------------------------

summary(lm(wb_modularity_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.211290 -0.048462  0.009251  0.050565  0.145048 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.54450    0.07860   6.928  1.8e-09 ***
## group1        -0.13213    0.10527  -1.255   0.2137    
## actamp        -0.08520    0.04972  -1.714   0.0911 .  
## group1:actamp  0.08812    0.06743   1.307   0.1956    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07326 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.04223,    Adjusted R-squared:  0.0005921 
## F-statistic: 1.014 on 3 and 69 DF,  p-value: 0.3918
summary(lm(wb_efficiency_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.03311 -0.25231 -0.07239  0.24181  1.28615 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     2.2954     0.4606   4.984 4.43e-06 ***
## group1          0.1922     0.6168   0.312   0.7563    
## actamp          0.5822     0.2913   1.998   0.0496 *  
## group1:actamp  -0.3051     0.3951  -0.772   0.4427    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4293 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1672, Adjusted R-squared:  0.131 
## F-statistic: 4.619 on 3 and 69 DF,  p-value: 0.005293
summary(lm(wb_clustering_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10405 -0.02538  0.00498  0.02738  0.07399 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.16668    0.03727   4.473 2.96e-05 ***
## group1         0.08089    0.04991   1.621    0.110    
## actamp         0.03898    0.02357   1.654    0.103    
## group1:actamp -0.05003    0.03197  -1.565    0.122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03473 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.04371,    Adjusted R-squared:  0.002127 
## F-statistic: 1.051 on 3 and 69 DF,  p-value: 0.3756
summary(lm(wb_participation_x ~ group * actamp, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.079717 -0.021283 -0.002828  0.019273  0.088921 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.58595    0.03592  16.314   <2e-16 ***
## group1        -0.09127    0.04810  -1.897    0.062 .  
## actamp        -0.02951    0.02272  -1.299    0.198    
## group1:actamp  0.06830    0.03081   2.217    0.030 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03348 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1051, Adjusted R-squared:  0.06622 
## F-statistic: 2.702 on 3 and 69 DF,  p-value: 0.0522
summary(lm(wb_modularity_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + actamp, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23187 -0.04277  0.01191  0.05436  0.12937 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.46960    0.05405   8.689 9.86e-13 ***
## group1       0.00357    0.01740   0.205    0.838    
## actamp      -0.03729    0.03376  -1.105    0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07363 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.01853,    Adjusted R-squared:  -0.009513 
## F-statistic: 0.6608 on 2 and 70 DF,  p-value: 0.5197
summary(lm(wb_efficiency_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + actamp, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0381 -0.2540 -0.0573  0.2743  1.3420 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2.5547     0.3142   8.131 1.05e-11 ***
## group1       -0.2776     0.1012  -2.744   0.0077 ** 
## actamp        0.4163     0.1962   2.122   0.0374 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.428 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:   0.16,  Adjusted R-squared:  0.136 
## F-statistic: 6.668 on 2 and 70 DF,  p-value: 0.002235
summary(lm(wb_clustering_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.104876 -0.025971  0.002049  0.026956  0.062811 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.209204   0.025760   8.121 1.09e-11 ***
## group1      0.003850   0.008295   0.464    0.644    
## actamp      0.011783   0.016088   0.732    0.466    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03509 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.009767,   Adjusted R-squared:  -0.01852 
## F-statistic: 0.3452 on 2 and 70 DF,  p-value: 0.7093
summary(lm(wb_participation_x ~ group + actamp, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + actamp, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.090585 -0.023412  0.000732  0.023682  0.082889 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.527893   0.025251  20.905   <2e-16 ***
## group1      0.013908   0.008131   1.710   0.0916 .  
## actamp      0.007625   0.015771   0.484   0.6302    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0344 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.04141,    Adjusted R-squared:  0.01402 
## F-statistic: 1.512 on 2 and 70 DF,  p-value: 0.2276
#---------------------------------------------------------

summary(lm(wb_modularity_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.209019 -0.041148  0.008544  0.058477  0.122391 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.458294   0.134478   3.408   0.0011 **
## group1        -0.106014   0.187217  -0.566   0.5731   
## actphi        -0.002865   0.008164  -0.351   0.7267   
## group1:actphi  0.007198   0.011935   0.603   0.5484   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0746 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.006755,   Adjusted R-squared:  -0.03643 
## F-statistic: 0.1564 on 3 and 69 DF,  p-value: 0.9252
summary(lm(wb_efficiency_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * actphi, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0192 -0.2406 -0.1013  0.2918  1.4750 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    3.41479    0.79436   4.299 5.53e-05 ***
## group1         0.33591    1.10589   0.304    0.762    
## actphi        -0.01274    0.04822  -0.264    0.792    
## group1:actphi -0.04396    0.07050  -0.624    0.535    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4407 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1224, Adjusted R-squared:  0.08421 
## F-statistic: 3.207 on 3 and 69 DF,  p-value: 0.02837
summary(lm(wb_clustering_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.101496 -0.024834  0.001338  0.027954  0.055959 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.252384   0.063247   3.990 0.000162 ***
## group1         0.050674   0.088050   0.576 0.566821    
## actphi        -0.001509   0.003840  -0.393 0.695567    
## group1:actphi -0.003341   0.005613  -0.595 0.553703    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03509 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.0242, Adjusted R-squared:  -0.01823 
## F-statistic: 0.5704 on 3 and 69 DF,  p-value: 0.6364
summary(lm(wb_participation_x ~ group * actphi, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.083502 -0.020964  0.001803  0.021042  0.080478 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.595250   0.060896   9.775 1.18e-14 ***
## group1        -0.143362   0.084777  -1.691   0.0953 .  
## actphi        -0.003378   0.003697  -0.914   0.3640    
## group1:actphi  0.010192   0.005405   1.886   0.0635 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03378 on 69 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.08869,    Adjusted R-squared:  0.04907 
## F-statistic: 2.238 on 3 and 69 DF,  p-value: 0.09149
summary(lm(wb_modularity_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.212347 -0.042878  0.009458  0.054667  0.120951 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.4030287  0.0979778   4.113 0.000105 ***
## group1      0.0062688  0.0196570   0.319 0.750744    
## actphi      0.0005034  0.0059281   0.085 0.932575    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07426 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.001519,   Adjusted R-squared:  -0.02701 
## F-statistic: 0.05324 on 2 and 70 DF,  p-value: 0.9482
summary(lm(wb_efficiency_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + actphi, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9682 -0.2485 -0.1034  0.2937  1.4635 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.75233    0.57886   6.482 1.08e-08 ***
## group1      -0.34989    0.11614  -3.013   0.0036 ** 
## actphi      -0.03331    0.03502  -0.951   0.3448    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4388 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.1174, Adjusted R-squared:  0.0922 
## F-statistic: 4.656 on 2 and 70 DF,  p-value: 0.01263
summary(lm(wb_clustering_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.097625 -0.025016  0.001984  0.026663  0.058204 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.278032   0.046077   6.034 6.82e-08 ***
## group1      -0.001435   0.009244  -0.155    0.877    
## actphi      -0.003072   0.002788  -1.102    0.274    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03493 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.01919,    Adjusted R-squared:  -0.008832 
## F-statistic: 0.6848 on 2 and 70 DF,  p-value: 0.5075
summary(lm(wb_participation_x ~ group + actphi, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + actphi, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.090350 -0.022181  0.000444  0.024979  0.085630 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.516995   0.045377  11.393   <2e-16 ***
## group1      0.015629   0.009104   1.717   0.0904 .  
## actphi      0.001391   0.002745   0.507   0.6141    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03439 on 70 degrees of freedom
##   (61 observations deleted due to missingness)
## Multiple R-squared:  0.04172,    Adjusted R-squared:  0.01434 
## F-statistic: 1.524 on 2 and 70 DF,  p-value: 0.225
#---------------------------------------------------------
summary(lm(wb_modularity_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.19544 -0.04638  0.00970  0.04419  0.12932 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.2764930  0.0777423   3.557 0.000674 ***
## group1             0.0327767  0.1043550   0.314 0.754375    
## sleep_time         0.0004103  0.0002341   1.753 0.083980 .  
## group1:sleep_time -0.0001028  0.0003040  -0.338 0.736297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07178 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07489,    Adjusted R-squared:  0.0358 
## F-statistic: 1.916 on 3 and 71 DF,  p-value: 0.1348
summary(lm(wb_efficiency_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * sleep_time, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0039 -0.2935 -0.1046  0.2380  1.2424 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        4.575575   0.445735  10.265 1.13e-15 ***
## group1            -1.238386   0.598318  -2.070  0.04211 *  
## sleep_time        -0.004169   0.001342  -3.107  0.00272 ** 
## group1:sleep_time  0.002894   0.001743   1.661  0.10116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4115 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2381, Adjusted R-squared:  0.2059 
## F-statistic: 7.397 on 3 and 71 DF,  p-value: 0.0002215
summary(lm(wb_clustering_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.101415 -0.023697  0.001451  0.025436  0.067102 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.3392422  0.0357120   9.499 2.82e-14 ***
## group1            -0.0869779  0.0479369  -1.814  0.07384 .  
## sleep_time        -0.0003397  0.0001075  -3.159  0.00232 ** 
## group1:sleep_time  0.0002743  0.0001396   1.965  0.05338 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03297 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1295, Adjusted R-squared:  0.09269 
## F-statistic:  3.52 on 3 and 71 DF,  p-value: 0.01932
summary(lm(wb_participation_x ~ group * sleep_time, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.077689 -0.022672 -0.000063  0.020843  0.076305 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        4.901e-01  3.725e-02  13.156   <2e-16 ***
## group1             4.100e-02  5.000e-02   0.820    0.415    
## sleep_time         1.513e-04  1.122e-04   1.349    0.182    
## group1:sleep_time -8.327e-05  1.456e-04  -0.572    0.569    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03439 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07872,    Adjusted R-squared:  0.03979 
## F-statistic: 2.022 on 3 and 71 DF,  p-value: 0.1185
summary(lm(wb_modularity_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.192925 -0.046353  0.008685  0.040878  0.137285 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.2965163  0.0500498   5.924 9.87e-08 ***
## group1      -0.0020315  0.0169195  -0.120   0.9048    
## sleep_time   0.0003493  0.0001484   2.354   0.0213 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07133 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0734, Adjusted R-squared:  0.04766 
## F-statistic: 2.852 on 2 and 72 DF,  p-value: 0.06429
summary(lm(wb_efficiency_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + sleep_time, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.02315 -0.29190 -0.06556  0.28016  1.34108 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.0116151  0.2922461  13.727  < 2e-16 ***
## group1      -0.2580073  0.0987946  -2.612  0.01096 *  
## sleep_time  -0.0024527  0.0008666  -2.830  0.00602 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4165 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2085, Adjusted R-squared:  0.1865 
## F-statistic: 9.485 on 2 and 72 DF,  p-value: 0.0002206
summary(lm(wb_clustering_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.103243 -0.023604 -0.002162  0.023968  0.077737 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.858e-01  2.359e-02  12.116   <2e-16 ***
## group1       5.936e-03  7.974e-03   0.744   0.4591    
## sleep_time  -1.770e-04  6.995e-05  -2.531   0.0136 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03362 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.08215,    Adjusted R-squared:  0.05665 
## F-statistic: 3.222 on 2 and 72 DF,  p-value: 0.04569
summary(lm(wb_participation_x ~ group + sleep_time, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + sleep_time, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.081169 -0.022639 -0.000012  0.022236  0.078857 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.063e-01  2.402e-02  21.081   <2e-16 ***
## group1      1.279e-02  8.119e-03   1.575    0.120    
## sleep_time  1.020e-04  7.122e-05   1.432    0.157    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03423 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07448,    Adjusted R-squared:  0.04877 
## F-statistic: 2.897 on 2 and 72 DF,  p-value: 0.06165
#---------------------------------------------------------
summary(lm(wb_modularity_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.209852 -0.044391  0.009905  0.053546  0.120870 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.279994   0.136840   2.046   0.0444 *
## group1             0.104516   0.187687   0.557   0.5794  
## efficiency         0.001831   0.001901   0.963   0.3388  
## group1:efficiency -0.001367   0.002598  -0.526   0.6004  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07403 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.01591,    Adjusted R-squared:  -0.02568 
## F-statistic: 0.3825 on 3 and 71 DF,  p-value: 0.7659
summary(lm(wb_efficiency_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * efficiency, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.03525 -0.28132 -0.07924  0.21062  1.56212 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        4.43758    0.80325   5.525 5.13e-07 ***
## group1            -1.26970    1.10173  -1.152    0.253    
## efficiency        -0.01718    0.01116  -1.539    0.128    
## group1:efficiency  0.01328    0.01525   0.871    0.387    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4345 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1505, Adjusted R-squared:  0.1146 
## F-statistic: 4.193 on 3 and 71 DF,  p-value: 0.008643
summary(lm(wb_clustering_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.103783 -0.026010 -0.001073  0.026582  0.064212 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.3431103  0.0631793   5.431 7.44e-07 ***
## group1            -0.0418171  0.0866557  -0.483   0.6309    
## efficiency        -0.0016102  0.0008777  -1.835   0.0708 .  
## group1:efficiency  0.0006107  0.0011996   0.509   0.6123    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03418 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.06452,    Adjusted R-squared:  0.02499 
## F-statistic: 1.632 on 3 and 71 DF,  p-value: 0.1896
summary(lm(wb_participation_x ~ group * efficiency, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.089014 -0.020775  0.000883  0.022435  0.080310 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.5066919  0.0636154   7.965 1.94e-11 ***
## group1            -0.0352984  0.0872539  -0.405    0.687    
## efficiency         0.0004619  0.0008838   0.523    0.603    
## group1:efficiency  0.0006985  0.0012079   0.578    0.565    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03441 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07748,    Adjusted R-squared:  0.0385 
## F-statistic: 1.988 on 3 and 71 DF,  p-value: 0.1236
summary(lm(wb_modularity_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.204226 -0.045892  0.007179  0.056812  0.119444 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.332480   0.093205   3.567 0.000646 ***
## group1      0.006171   0.017058   0.362 0.718605    
## efficiency  0.001099   0.001289   0.852 0.396920    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07366 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.01207,    Adjusted R-squared:  -0.01537 
## F-statistic: 0.4398 on 2 and 72 DF,  p-value: 0.6459
summary(lm(wb_efficiency_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + efficiency, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0416 -0.2806 -0.1041  0.2616  1.5290 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.927800   0.548956   7.155 5.72e-10 ***
## group1      -0.314520   0.100469  -3.131  0.00252 ** 
## efficiency  -0.010068   0.007594  -1.326  0.18912    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4338 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1414, Adjusted R-squared:  0.1176 
## F-statistic:  5.93 on 2 and 72 DF,  p-value: 0.004131
summary(lm(wb_clustering_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.104078 -0.026216 -0.000067  0.026204  0.063711 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.3196655  0.0430274   7.429 1.77e-10 ***
## group1       0.0021122  0.0078748   0.268   0.7893    
## efficiency  -0.0012833  0.0005952  -2.156   0.0344 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.034 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.0611, Adjusted R-squared:  0.03502 
## F-statistic: 2.343 on 2 and 72 DF,  p-value: 0.1033
summary(lm(wb_participation_x ~ group + efficiency, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + efficiency, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.089550 -0.021887 -0.000356  0.021168  0.077220 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.4798759  0.0433473  11.070   <2e-16 ***
## group1      0.0149474  0.0079333   1.884   0.0636 .  
## efficiency  0.0008358  0.0005997   1.394   0.1677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03426 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.07314,    Adjusted R-squared:  0.04739 
## F-statistic: 2.841 on 2 and 72 DF,  p-value: 0.06495
#---------------------------------------------------------
summary(lm(wb_modularity_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group * total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.17886 -0.05088  0.01280  0.05125  0.11686 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      4.607e-01  2.788e-02  16.526   <2e-16 ***
## group1           3.937e-02  4.934e-02   0.798   0.4276    
## total_ac        -3.281e-07  1.697e-07  -1.933   0.0572 .  
## group1:total_ac -3.107e-07  3.472e-07  -0.895   0.3738    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07059 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1053, Adjusted R-squared:  0.06746 
## F-statistic: 2.784 on 3 and 71 DF,  p-value: 0.04702
summary(lm(wb_efficiency_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group * total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.95626 -0.24882 -0.04928  0.23203  1.43588 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      2.815e+00  1.625e-01  17.327   <2e-16 ***
## group1          -3.898e-01  2.876e-01  -1.355   0.1796    
## total_ac         2.592e-06  9.891e-07   2.621   0.0107 *  
## group1:total_ac  9.943e-07  2.024e-06   0.491   0.6247    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4114 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2384, Adjusted R-squared:  0.2062 
## F-statistic: 7.409 on 3 and 71 DF,  p-value: 0.0002186
summary(lm(wb_clustering_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group * total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.100155 -0.025819  0.001346  0.026926  0.065194 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      2.066e-01  1.366e-02  15.128   <2e-16 ***
## group1           1.276e-02  2.418e-02   0.528   0.5992    
## total_ac         1.394e-07  8.315e-08   1.676   0.0981 .  
## group1:total_ac -6.357e-08  1.701e-07  -0.374   0.7098    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03459 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.04191,    Adjusted R-squared:  0.001429 
## F-statistic: 1.035 on 3 and 71 DF,  p-value: 0.3823
summary(lm(wb_participation_x ~ group * total_ac, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group * total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.090277 -0.021863  0.000954  0.022384  0.085116 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.308e-01  1.375e-02  38.606   <2e-16 ***
## group1           2.932e-02  2.434e-02   1.205    0.232    
## total_ac         5.965e-08  8.370e-08   0.713    0.478    
## group1:total_ac -9.860e-08  1.713e-07  -0.576    0.567    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03482 on 71 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.05579,    Adjusted R-squared:  0.01589 
## F-statistic: 1.398 on 3 and 71 DF,  p-value: 0.2504
summary(lm(wb_modularity_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = wb_modularity_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.19185 -0.05267  0.01385  0.04992  0.11678 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.719e-01  2.489e-02  18.958  < 2e-16 ***
## group1      -2.195e-03  1.664e-02  -0.132  0.89541    
## total_ac    -4.023e-07  1.478e-07  -2.721  0.00815 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07049 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.09517,    Adjusted R-squared:  0.07004 
## F-statistic: 3.786 on 2 and 72 DF,  p-value: 0.02732
summary(lm(wb_efficiency_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = wb_efficiency_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.94760 -0.23446 -0.04844  0.22636  1.43164 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.780e+00  1.445e-01  19.235  < 2e-16 ***
## group1      -2.567e-01  9.660e-02  -2.658  0.00968 ** 
## total_ac     2.830e-06  8.583e-07   3.297  0.00152 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4093 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2358, Adjusted R-squared:  0.2146 
## F-statistic: 11.11 on 2 and 72 DF,  p-value: 6.236e-05
summary(lm(wb_clustering_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = wb_clustering_x ~ group + total_ac, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.100708 -0.026059  0.000974  0.026267  0.067953 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.089e-01  1.214e-02  17.210   <2e-16 ***
## group1      4.261e-03  8.115e-03   0.525   0.6011    
## total_ac    1.242e-07  7.211e-08   1.722   0.0894 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03438 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.04003,    Adjusted R-squared:  0.01336 
## F-statistic: 1.501 on 2 and 72 DF,  p-value: 0.2298
summary(lm(wb_participation_x ~ group + total_ac, data = d))
## 
## Call:
## lm(formula = wb_participation_x ~ group + total_ac, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.08952 -0.02210  0.00164  0.02457  0.08473 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.344e-01  1.224e-02  43.670   <2e-16 ***
## group1      1.613e-02  8.180e-03   1.972   0.0525 .  
## total_ac    3.610e-08  7.268e-08   0.497   0.6209    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03465 on 72 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.05138,    Adjusted R-squared:  0.02503 
## F-statistic:  1.95 on 2 and 72 DF,  p-value: 0.1497
d3.mlt <- melt(select(d, record_id, group, dmn_participation_x, IS:fact, -L5_starttime, -M10_starttime, efficiency:total_ac), id.vars=c('record_id', 'group', 'dmn_participation_x'))

ggplot(data = d3.mlt) + 
  ggtitle('DMN Participation Coefficient and Rest-Activity Measures') + 
  geom_point(aes(x = value, y = dmn_participation_x, group = group, color = group), size=0.5) + 
  #stat_smooth(aes(x = value, y = dmn_participation_x, group = group, color = group), method = 'lm', se = FALSE, fullrange = FALSE) +
  facet_wrap(~ variable,  scales='free') + 
  theme_minimal() +
  scale_color_manual(values = brewer.pal(8, "Paired")[7:8], name = 'Group', labels = c('Young Adults', 'Older Adults')) + 
  xlab(element_blank()) + ylab('DMN Participation Coefficient') +
  theme(axis.text.y = element_blank(), axis.text.x = element_blank())
## Warning: Removed 1266 rows containing missing values (geom_point).

ggplot(data = d) + 
  ggtitle('DMN Participation Coefficient and Amplitude') + 
  geom_point(aes(x = actamp, y = dmn_participation_x, group = group, color = group), size=2) + 
  stat_smooth(aes(x = actamp, y = dmn_participation_x, group = group, color = group), method = 'lm', se = FALSE, fullrange = FALSE) +
  theme_minimal() +
  scale_color_manual(values = brewer.pal(8, "Paired")[7:8], name = 'Group', labels = c('Young Adults', 'Older Adults')) + 
  xlab('Amplitude') + ylab('DMN Participation Coefficient') 
## Warning: Removed 61 rows containing non-finite values (stat_smooth).
## Warning: Removed 61 rows containing missing values (geom_point).

ggplot(data = d) + 
  ggtitle('DMN Participation Coefficient and Interdaily Stability') + 
  geom_point(aes(x = IS, y = dmn_participation_x, group = group, color = group), size=2) + 
  stat_smooth(aes(x = IS, y = dmn_participation_x, group = group, color = group), method = 'lm', se = FALSE, fullrange = FALSE) +
  theme_minimal() +
  scale_color_manual(values = brewer.pal(8, "Paired")[7:8], name = 'Group', labels = c('Young Adults', 'Older Adults')) + 
  xlab('IS') + ylab('DMN Participation Coefficient') 
## Warning: Removed 60 rows containing non-finite values (stat_smooth).
## Warning: Removed 60 rows containing missing values (geom_point).

ggplot(data = d) + 
  ggtitle('DMN Participation Coefficient and Total Sleep Time') + 
  geom_point(aes(x = sleep_time, y = dmn_participation_x, group = group, color = group), size=2) + 
  stat_smooth(aes(x = sleep_time, y = dmn_participation_x, group = group, color = group), method = 'lm', se = FALSE, fullrange = FALSE) +
  theme_minimal() +
  scale_color_manual(values = brewer.pal(8, "Paired")[7:8], name = 'Group', labels = c('Young Adults', 'Older Adults')) + 
  xlab('Sleep Time (min)') + ylab('DMN Participation Coefficient') + 
  scale_x_continuous(limits = c(125, 550))
## Warning: Removed 59 rows containing non-finite values (stat_smooth).
## Warning: Removed 59 rows containing missing values (geom_point).

plot(lm(dmn_participation_x ~ actamp, data = d))

summary(lm(trails_b_z_score ~ group + actamp, data = d))
## 
## Call:
## lm(formula = trails_b_z_score ~ group + actamp, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5327 -0.7351  0.1222  1.0960  2.5738 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -3.5357     0.8597  -4.113 7.61e-05 ***
## group1        0.6526     0.2773   2.353 0.020397 *  
## actamp        1.9839     0.5345   3.712 0.000326 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.444 on 109 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1335, Adjusted R-squared:  0.1176 
## F-statistic: 8.399 on 2 and 109 DF,  p-value: 0.000405
plot(lm(trails_b_z_score ~ group + actamp, data = d))

summary(lm(trails_b_z_score ~ group + fact, data = d))
## 
## Call:
## lm(formula = trails_b_z_score ~ group + fact, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9129 -0.6277  0.1647  1.1082  2.2264 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -1.138e+00  3.525e-01  -3.229  0.00164 **
## group1       4.212e-01  2.830e-01   1.488  0.13953   
## fact         1.825e-04  7.421e-05   2.459  0.01549 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.492 on 109 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.07535,    Adjusted R-squared:  0.05838 
## F-statistic: 4.441 on 2 and 109 DF,  p-value: 0.01399
summary(lm(trails_b_z_score ~ fact, data = d))
## 
## Call:
## lm(formula = trails_b_z_score ~ fact, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6847 -0.6959  0.1140  1.2130  2.4284 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -9.544e-01  3.320e-01  -2.875  0.00486 **
## fact         1.910e-04  7.439e-05   2.568  0.01158 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.5 on 110 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.05655,    Adjusted R-squared:  0.04798 
## F-statistic: 6.594 on 1 and 110 DF,  p-value: 0.01158
plot(lm(trails_b_z_score ~ fact, data = d))